# NTIRE 2024 Challenge on Low Light Image Enhancement: Methods and Results

Xiaoning Liu∗ Zongwei Wu∗ Ao Li∗ Florin-Alexandru Vasluianu∗ Yulun Zhang∗
Shuhang Gu∗ Le Zhang∗ Ce Zhu∗ Radu Timofte∗ Zhi Jin  Hongjun Wu  Chenxi Wang
Haitao Ling  Yuanhao Cai  Hao Bian  Yuxin Zheng  Jing Lin  Alan Yuille  Ben
Shao  Jin Guo  Tianli Liu  Mohao Wu  Yixu Feng  Shuo Hou  Haotian Lin  Yu Zhu
Peng Wu  Wei Dong  Jinqiu Sun  Yanning Zhang  Qingsen Yan  Wenbin Zou  Weipeng
Yang  Yunxiang Li  Qiaomu Wei  Tian Ye  Sixiang Chen  Zhao Zhang  Suiyi Zhao
Bo Wang  Yan Luo  Zhichao Zuo  Mingshen Wang  Junhu Wang  Yanyan Wei  Xiaopeng
Sun  Yu Gao  Jiancheng Huang  Hongming Chen  Xiang Chen  Hui Tang  Yuanbin
Chen  Yuanbo Zhou  Xinwei Dai  Xintao Qiu  Wei Deng  Qinquan Gao  Tong Tong
Mingjia Li  Jin Hu  Xinyu He  Xiaojie Guo  Sabarinathan  K Uma  A Sasithradevi
B Sathya Bama  S. Mohamed Mansoor Roomi  V.Srivatsav  Jinjuan Wang  Long Sun
Qiuying Chen  Jiahong Shao  Yizhi Zhang  Marcos V. Conde  Daniel Feijoo  Juan
C. Benito  Alvaro García  Jaeho Lee  Seongwan Kim  Sharif S M A  Nodirkhuja
Khujaev  Roman Tsoy  Ali Murtaza  Uswah Khairuddin  Ahmad ’Athif Mohd Faudzi
Sampada Malagi  Amogh Joshi  Nikhil Akalwadi  Chaitra Desai  Ramesh Ashok
Tabib  Uma Mudenagudi  Wenyi Lian  Wenjing Lian  Jagadeesh Kalyanshetti
Vijayalaxmi Ashok Aralikatti  Palani Yashaswini  Nitish Upasi  Dikshit Hegde
Ujwala Patil  Sujata C  Xingzhuo Yan  Wei Hao  Minghan Fu  Pooja choksy
Anjali Sarvaiya  Kishor Upla  Kiran Raja  Hailong Yan  Yunkai Zhang  Baiang Li
Jingyi Zhang  Huan Zheng

###### Abstract

This paper reviews the NTIRE 2024 low light image enhancement challenge,
highlighting the proposed solutions and results. The aim of this challenge is
to discover an effective network design or solution capable of generating
brighter, clearer, and visually appealing results when dealing with a variety
of conditions, including ultra-high resolution (4K and beyond), non-uniform
illumination, backlighting, extreme darkness, and night scenes. A notable
total of 428 participants registered for the challenge, with 22 teams
ultimately making valid submissions. This paper meticulously evaluates the
state-of-the-art advancements in enhancing low-light images, reflecting the
significant progress and creativity in this field.

††footnotetext: ∗ X. Liu, Z. Wu, A. Li, F. Vasluianu, Y. Zhang, S. Gu, L.
Zhang, C. Zhu and R. Timofte were the challenge organizers, while the other
authors participated in the challenge. Each team described their own method in
the report. Appendix A contains the authors’ teams and affiliations.
NTIRE 2024 webpage: https://cvlai.net/ntire/2024.
Code: https://github.com/AVC2-UESTC/NTIRE24-LLE/

##  1 Introduction

Low light Image enhancement (LLIE), a pivotal yet challenging task in computer
vision, aims to improve visibility and contrast across a broad spectrum of
low-light scenarios, including uneven illumination, extreme darkness,
backlighting, and night. Additionally, LLIE strives to correct imperfections
like noise, artifacts, and color distortion. These challenges, arising in
darkness or through illumination enhancement, affect both human visual
perception and downstream tasks like object detection and scene segmentation.

As deep learning technology improves by leaps and bounds, remarkable advances
in LLIE are impressive. However, current cutting-edge methods [34, 8, 39, 72,
55, 47, 78] not only struggle to adapt to complex and variable low-light
conditions but also face major challenges when being deployed on consumer-
grade devices such as smartphones and cameras. This is primarily constrained
by the limitations of current datasets [71, 7] that suffer from limited scene
diversity (notably insufficient night scenes), low resolution, and overly
simplistic lighting conditions. Additionally, the high complexity of models
[8, 38, 74, 69, 39] hampers their ability to handle ultra-high resolution
images that are commonly captured by smartphones.

To address the aforementioned challenges, we launched the inaugural Low Light
Enhancement Challenge at the 2024 New Trends in Image Restoration and
Enhancement (NTIRE 2024) workshop. The objective of this challenge is to
foster innovative thinking and discover solutions that significantly improve
image quality under various low-light conditions. To this end, we have built
an LLIE dataset that features a wide range of scenes, encompassing various
low-light conditions. These images include indoor and outdoor locations under
both daylight and nighttime conditions, with the challenge focusing on
enhancing image quality across these diverse settings.

In conclusion, this challenge aims to set a new benchmark for LLIE while
highlighting specific challenges and research questions in this domain. We
hope that it will inspire the research community to explore these pressing
issues and identify emerging trends. Moreover, this challenge is one of the
NTIRE 2024 Workshop associated challenges on: dense and non-homogeneous
dehazing [1], night photography rendering [4], blind compressed image
enhancement [73], shadow removal [67], efficient super resolution [64], image
super resolution ($\times$4) [20], light field image super-resolution [70],
stereo image super-resolution [68], HR depth from images of specular and
transparent surfaces [77], bracketing image restoration and enhancement [81],
portrait quality assessment [17], quality assessment for AI-generated content
[48], restore any image model (RAIM) in the wild [45], RAW image super-
resolution [24], short-form UGC video quality assessment [44], low light
enhancement [49], and RAW burst alignment and ISP challenge.

##  2 NTIRE 2024 Low Light Enhancement Challenge

The objectives of this challenge are threefold: (1) to advance research in the
field of low light enhancement, (2) to facilitate systematic comparisons among
different methodologies, and (3) to provide a forum for both academic and
industrial stakeholders to interact, deliberate, and potentially forge
partnerships. This section delves into the detailed aspects of the challenge.

###  2.1 Dataset

Although some low light datasets, such as LOL [71] and MIT-Adobe FiveK [7],
have been widely applied in the field, they exhibit limitations previously
mentioned, including limited resolution, monotonous scene content, and uniform
illumination levels. These factors often restrict the generalization
capabilities of cutting-edge models [8, 39, 72, 55, 47, 78], posing
significant challenges in adapting to diverse low-light conditions, especially
when implemented on consumer-grade devices like smartphones and cameras. To
address these issues, this challenge introduces a rich array of contest
scenarios, covering a variety of lighting conditions such as dim environments,
extreme darkness, non-uniform illumination, backlighting, and night scenes,
applicable to both indoor and outdoor settings during day and night, with
image resolutions up to 4K and beyond. Specifically, the dataset includes 230
training scenes, along with 35 validation and 35 testing scenes. The ground
truth (GT) images for both validation and testing were kept concealed from the
participants throughout the challenge. Further details about the dataset will
be provided in subsequent works.

###  2.2 Tracks and Competition

Ranking statistic. In this challenge, we primarily use peak signal-to-noise
ratio (PSNR), structural similarity (SSIM), and Learned Perceptual Image Patch
Similarity (LPIPS) [80] as the criteria for comparing models submitted by
participants. Given that LPIPS is a learned perceptual evaluation metric and
our competition focuses mainly on quantitative assessments, We use LPIPS as a
supplementary evaluation tool when the quantitative evaluations of two methods
are indistinguishable. As listed in Tab. 1, “Final Rank” represents a
composite metric, calculated through a weighted sum of PSNR (60%) and SSIM
(40%). This way offers a comprehensive evaluation of the effectiveness of each
solution in enhancing low-light conditions.

Challenge phases.  (1) Development and validation phase: Participants were
granted access to 230 training image pairs and 35 validation inputs from our
constructed dataset. It is noteworthy that the GT images for the validation
set were hidden from the participants. Participants had the opportunity to
submit their enhanced results to the evaluation server, which calculated the
PSNR and SSIM for the enhanced images generated by their models and provided
immediate feedback. (2) Testing phase: Participants gained access to 35
testing low light images from the built dataset, with the ground-truth images
remaining undisclosed. Submissions of their enhanced outputs were made to the
Codalab evaluation server, accompanied by an email to the organizers
containing the code and a factsheet. The organizers subsequently verified and
executed the provided code to derive the final results, which were shared with
participants upon the conclusion of the challenge.

Table 1: Evaluation and Rankings in the NTIRE 2024 Low Light Enhancement Challenge. This table presents a comprehensive comparison of participant solutions across multiple metrics: PSNR, SSIM, and LPIPS. “Rank PSNR” and “Rank SSIM” indicate the respective standings of participants based on their performance in PSNR and SSIM metrics on the challenge’s test dataset. “Final Rank” represents a composite metric, derived from a weighted sum of PSNR (60%) and SSIM (40%), which provides an overall assessment of each solution’s effectiveness in low light enhancement. Team | PSNR | SSIM | LPIPS | Rank PSNR | Rank SSIM | Final Rank
---|---|---|---|---|---|---
SYSU-FVL-T2 | 25.52 | 0.8637 | 0.1221 | 1 | 1 | 1
Retinexformer [8] | 25.30 | 0.8525 | 0.1424 | 2 | 4 | 2
DH-AISP | 24.97 | 0.8528 | 0.1235 | 3 | 3 | 3
NWPU-DiffLight | 24.78 | 0.8556 | 0.1673 | 6 | 2 | 4
GiantPandaCV | 24.83 | 0.8474 | 0.1353 | 5 | 7 | 5
LVGroup_HFUT | 24.88 | 0.8395 | 0.1371 | 4 | 10 | 6
Try1try8 | 24.49 | 0.8483 | 0.1359 | 8 | 6 | 7
Pixel_warrior | 24.74 | 0.8416 | 0.1514 | 7 | 9 | 8
HuiT | 24.13 | 0.8484 | 0.1436 | 10 | 5 | 9
X-LIME | 24.28 | 0.8446 | 0.1298 | 9 | 8 | 10
Image Lab | 23.63 | 0.8235 | 0.1673 | 11 | 12 | 11
dgzzqteam | 23.28 | 0.8385 | 0.1406 | 12 | 11 | 12
Cidaut AI (InstructIR [23]) | 23.07 | 0.8075 | 0.1559 | 13 | 16 | 13
OptDev | 22.93 | 0.8097 | 0.1592 | 14 | 15 | 14
ataza | 22.51 | 0.8161 | 0.1404 | 18 | 13 | 15
KLETech-CEVI_LowlightHypnotise | 22.85 | 0.7828 | 0.1823 | 15 | 18 | 16
221B | 22.04 | 0.8141 | 0.1084 | 19 | 14 | 17
KLETech-CEVI_Dark_Knights | 22.76 | 0.7843 | 0.1806 | 17 | 17 | 18
BFU-LL | 22.78 | 0.7792 | 0.1826 | 16 | 19 | 19
SVNIT_NTNU | 20.32 | 0.7718 | 0.3089 | 20 | 20 | 20
yanhailong | 20.07 | 0.6881 | 0.3133 | 21 | 22 | 21
Mishka | 18.19 | 0.7161 | 0.2712 | 22 | 21 | 22

##  3 Challenge Results and Discussion

The results of the low light enhancement challenge are detailed in Tab. 1,
which evaluates and ranks the performances of 22 teams. Notably, one team,
AiRiA_Vision, voluntarily withdrew from the ranking due to issues related to
their model design. The evaluation leverages two key performance metrics: PSNR
and SSIM. The metrics are calculated based on a test set comprising 35 inputs
from the built dataset, thereby ensuring the challenge’s integrity and
mitigating the risk of overfitting to the validation set.

The top-ranked teams in the challenge boast higher PSNR and SSIM values,
signifying superior performance, while the lower-ranked teams exhibit lower
values, indicative of suboptimal performance. Notably, two teams achieved a
PSNR of over 25 dB, meeting our prior expectations for this metric. For more
detailed information on the low-light enhancement methods employed, please
refer to Sec. 4, which discusses the specific solutions provided by each team.

Due to the ultra-high resolution (4K and beyond) of the input images, many
teams opted for multi-scale strategies to implement enhancement. While they
has indeed reduced computational consumption to some extent, currently, almost
all models are unable to perform inference on a plain GPU,  _e.g._ , one with
12G memory. For the next competition, we will consider including NIQE [59]
along with metrics measuring model efficiency, such as inference time, model
parameter count, computational complexity (FLOPs), and memory consumption.
This will promote the application of ultra-high-resolution low light
enhancement on smartphones.

##  4 Challenge Methods and Teams

###  4.1 SYSU-FVL-T2

Figure 1: The backbone network employed in our method. (Reproduced from ESDNet
[75])

Description: As shown in Fig. 1, ESDNet-L [75] is employed as the backbone in
our proposed method for low-light image enhancement. The backbone mainly
consists of an encoder-decoder network in three feature scales with skip-
connections. Different scales of features are generated by adopting the
bilinear interpolation. In each scale, the Semantic-Aligned Scale-Aware
Modules (SAM) are stacked to enhance the ability of the model to address scale
variations. SAM incorporates a pyramid context extraction module and a cross-
scale dynamic fusion module to selectively fuse multi-scale features.

Furthermore, for network training, the loss function $L_{total}$ is set for
outputs in three scales $\hat{I}_{1},\hat{I}_{2},\hat{I}_{3}$:

$L_{total}=\sum_{i=1}^{3}{L_{C}\left(I_{i},\hat{I}_{i}\right)}+\lambda\cdot
L_{P}\left(I_{i},\hat{I}_{i}\right),$ (1)

where $\lambda$ is set as 0.04, ${I}_{i}$ stands for ground truth image,
$\hat{I}_{i}$ stands for enhanced image, $L_{C}$ stands for Charbonnier loss
[42], and $L_{P}$ stands for perceptual loss using pretrained VGG19 model
[66].

Implementation: The proposed method is based on Python 3.8 and the experiment
is conducted on one NVIDIA RTX A6000 GPU (49G). Inspired by MIRNet-v2 [79], we
adopt a progressive training strategy. The model is trained for 150000
iterations from scratch and optimized by Adam [41]. During the training
process, at the first stage, the batch size is set to 8, and we randomly crop
square image patches of size 720 for training. Subsequently, after 46000,
32000, 24000, 18000, and 18000 iterations, the batch size decreases to 4, 4,
2, 2, and 1, respectively. The lengths of the square image patches are set to
1024, 1024, 1280, 1280, and 1600 for the respective stages. The learning rate
is set as $2\times 10^{-4}$ initially and is scheduled by cyclic cosine
annealing [52]. During the testing phase, the whole low-light image is fed
into the network, and the enhanced image is obtained directly. The batchsize
in testing is set as 1.

###  4.2 Retinexformer

The proposed code, pre-trained models, results, and training logs are all
publicly available at https://github.com/caiyuanhao1998/Retinexformer.

---
Figure 2: The overview of Retinexformer [8]. (a) Retinexformer adopts the
proposed ORF that consists of an illumination estimator (i) and a corruption
restorer (ii) IGT. (b) The basic unit of IGT is IGAB, which is composed of two
layer normalization (LN), an IG-MSA and a feed-forward network (FFN). (c) IG-
MSA uses the illumination representations captured by ORF to direct the
computation of self-attention.

Description: The team directly adopt their ICCV 2023 work Retinexformer [8] to
participate this challenge. Fig. 2 illustrates the overall architecture of
their method. As shown in Fig. 2 (a), Retinexformer is based on their
formulated One-stage Retinex-based Framework (ORF). Their Retinexformer takes
a low-light image $\mathbf{I}\in\mathbb{R}^{H\times W\times 3}$ as input and
reconstruct its enhanced counterpart $\mathbf{I}_{en}\in\mathbb{R}^{H\times
W\times 3}$. The original Retinex model assumes that the low-light image
$\mathbf{I}$ is corruption-free and can be decomposed into a reflectance image
$\mathbf{R}\in\mathbb{R}^{H\times W\times 3}$ and an illumination map
$\mathbf{L}\in\mathbb{R}^{H\times W}$ as:

$\mathbf{I}=\mathbf{R}\odot\mathbf{L},$ (2)

where $\odot$ denotes the element-wise multiplication. However, this
corruption-free assumption is consistent with the real under-exposed scenes,
where corruptions are inevitably introduced by the high-ISO and long-exposure
imaging settings, as well as the light-up process. To model the corruptions,
they introduce perturbation terms for $\mathbf{R}$ and $\mathbf{L}$ and
reformulate Eq. 2 as:

$\displaystyle\mathbf{I}$
$\displaystyle=(\mathbf{R}+\mathbf{\hat{R}})\odot(\mathbf{L}+\mathbf{\hat{L}})$
(3)
$\displaystyle=\mathbf{R}\odot\mathbf{L}+\mathbf{R}\odot\mathbf{\hat{L}}+\mathbf{\hat{R}}\odot(\mathbf{L}+\mathbf{\hat{L}}).$

$\mathbf{\hat{R}}\in\mathbb{R}^{H\times W\times 3}$ and
$\mathbf{\hat{L}}\in\mathbb{R}^{H\times W}$ are the perturbations. To light up
$\mathbf{I}$, they multiply two sides of Eq. 3 by a light-up map
$\mathbf{\bar{L}}$ such that $\mathbf{\bar{L}}\odot\mathbf{{L}}=\mathbf{1}$
as:

$\mathbf{I}\odot\mathbf{\bar{L}}=\mathbf{R}+\mathbf{R}\odot(\mathbf{\hat{L}}\odot\mathbf{\bar{L}})+(\mathbf{\hat{R}}\odot(\mathbf{L}+\mathbf{\hat{L}}))\odot\mathbf{\bar{L}},$
(4)

They then simplify Eq. 4 as

$\mathbf{I}_{{lu}}=\mathbf{I}\odot\mathbf{\bar{L}}=\mathbf{R}+\mathbf{C},$ (5)

where $\mathbf{I}_{{lu}}\in\mathbb{R}^{H\times W\times 3}$ denotes the lit-up
image and $\mathbf{C}\in\mathbb{R}^{H\times W\times 3}$ indicates the overall
corruption term. Subsequently, they formulate their ORF as:

$(\mathbf{I}_{{lu}},\mathbf{F}_{{lu}})=\mathcal{E}(\mathbf{I},\mathbf{L}_{p}),~{}~{}~{}~{}\mathbf{I}_{en}=\mathcal{R}(\mathbf{I}_{{lu}},\mathbf{F}_{{lu}}),$
(6)

where $\mathcal{E}$ denotes the illumination estimator and $\mathcal{R}$
represents the corruption restorer. $\mathcal{E}$ takes $\mathbf{I}$ and its
illumination prior map $\mathbf{L}_{p}\in\mathbb{R}^{H\times W}$ as inputs.
$\mathbf{L}_{p}=\text{mean}_{c}(\mathbf{I})$ where $\text{mean}_{c}$
calculates the average pixel values across channels. $\mathcal{E}$ outputs the
lit-up image $\mathbf{I}_{lu}$ and light-up feature
$\mathbf{F}_{lu}\in\mathbb{R}^{H\times W\times C}$. Then $\mathbf{I}_{lu}$ and
$\mathbf{F}_{lu}$ are fed into $\mathcal{R}$ to restore the corruptions and
produce the enhanced image $\mathbf{I}_{en}\in\mathbb{R}^{H\times W\times 3}$.

Illumination Estimator. The architecture of $\mathcal{E}$ is shown in Fig. 2
(a) (i). $\mathcal{E}$ firstly uses a $conv$1$\times$1 (convolution with
kernel size = 1) to fuse the concatenation of $\mathbf{I}$ and
$\mathbf{L}_{p}$. The well-exposed regions can provide semantic contextual
information for under-exposed regions. Thus, a depth-wise separable
$conv$9$\times$9 is adopted to model the interactions of regions with
different lighting conditions to generate the light-up feature
$\mathbf{F}_{lu}$. Then $\mathcal{E}$ uses a $conv$1$\times$1 to aggregate
$\mathbf{F}_{lu}$ to produce the light-up map
$\mathbf{\bar{L}}\in\mathbb{R}^{H\times W\times 3}$, which is used to light up
$\mathbf{I}$ in Eq. 4.

Illumination-Guided Transformer. As illustrated in Fig. 2 (a) (ii), IGT adopts
a three-scale U-shaped architecture [10, 13, 16, 15, 14]. The input of IGT is
the lit-up image $\mathbf{I}_{lu}$. In the downsampling branch,
$\mathbf{I}_{lu}$ undergoes a $conv$3$\times$3, an IGAB, a strided
$conv$4$\times$4, two IGABs, and a strided $conv$4$\times$4 to generate
hierarchical features
$\mathbf{F}_{i}\in\mathbb{R}^{\frac{H}{2^{i}}\times\frac{W}{2^{i}}\times
2^{i}C}$ where $i$ = 0, 1, 2. Then $\mathbf{F}_{2}$ passes through two IGABs.
Subsequently, a symmetrical structure is designed as the upsampling branch.
Skip connections are used to alleviate the information loss caused by the
downsampling branch. The upsampling branch outputs a residual image
$\mathbf{I}_{re}\in\mathbb{R}^{H\times W\times 3}$. Then the enhanced image
$\mathbf{I}_{en}$ is derived by the sum of $\mathbf{I}_{lu}$ and
$\mathbf{I}_{re}$, _i.e._ , $\mathbf{I}_{en}$ = $\mathbf{I}_{lu}$ \+
$\mathbf{I}_{re}$.

IG-MSA. As illustrated in Fig. 2 (c), the light-up feature
$\mathbf{F}_{lu}\in\mathbb{R}^{H\times W\times C}$ estimated by $\mathcal{E}$
is fed into each IG-MSA of IGT. Firstly, the input feature is reshaped into
tokens $\mathbf{X}\in\mathbb{R}^{HW\times C}$ and split into $k$ heads:

$\mathbf{X}=[\mathbf{X}_{1},~{}\mathbf{X}_{2},~{}\cdots,~{}\mathbf{X}_{k}],$
(7)

where $\mathbf{X}_{i}\in\mathbb{R}^{HW\times d_{k}},d_{k}=\frac{C}{k},$ and
$i=1,2,\cdots,k$. Then $\mathbf{X}_{i}$ is linearly projected into $query$
$\mathbf{Q}_{i}$, _key_ $\mathbf{K}_{i}$, and _value_
$\mathbf{V}_{i}\in\mathbb{R}^{HW\times d_{k}}$ as:

$\mathbf{Q}_{i}=\mathbf{X}_{i}\mathbf{W}_{\mathbf{Q}_{i}}^{\text{T}},~{}~{}\mathbf{K}_{i}=\mathbf{X}_{i}\mathbf{W}_{\mathbf{K}_{i}}^{\text{T}},~{}~{}\mathbf{V}_{i}=\mathbf{X}_{i}\mathbf{W}_{\mathbf{V}_{i}}^{\text{T}},$
(8)

where $\mathbf{W}_{\mathbf{Q}_{i}}$, $\mathbf{W}_{\mathbf{K}_{i}}$, and
$\mathbf{W}_{\mathbf{V}_{i}}\in\mathbb{R}^{d_{k}\times d_{k}}$ are learnable
parameters and T denotes the matrix transpose. Subsequently, they use the
light-up feature $\mathbf{F}_{lu}$ encoding illumination information and
interactions of regions with different lighting conditions to direct the
computation of self-attention. They reshape $\mathbf{F}_{lu}$ into
$\mathbf{Y}\in\mathbb{R}^{HW\times C}$ and split it into $k$ heads:

$\mathbf{Y}=[\mathbf{Y}_{1},~{}\mathbf{Y}_{2},~{}\cdots,~{}\mathbf{Y}_{k}],$
(9)

where $\mathbf{Y}_{i}\in\mathbb{R}^{HW\times d_{k}},i=1,2,\cdots,k$. Then
self-attention is formulated as:

$\text{Attention}(\mathbf{Q}_{i},\mathbf{K}_{i},\mathbf{V}_{i},\mathbf{Y}_{i})=(\mathbf{Y}_{i}\odot\mathbf{V}_{i})\text{softmax}(\frac{\mathbf{K}_{i}^{\text{T}}\mathbf{Q}_{i}}{\alpha_{i}}),$
(10)

where $\alpha_{i}\in\mathbb{R}^{1}$ is learnable parameter. Subsequently, $k$
heads are concatenated to pass through an $fc$ layer and plus a learnable
positional encoding $\mathbf{P}\in\mathbb{R}^{HW\times C}$ to produce the
output tokens $\mathbf{X}_{out}\in\mathbb{R}^{HW\times C}$. Finally, they
reshape $\mathbf{X}_{out}$ to derive output feature
$\mathbf{F}_{out}\in\mathbb{R}^{H\times W\times C}$.

Besides Retinexformer [8], the team also adopt their winning solution MST [11,
12] of NTIRE 2022 Spectral Recovery Challenge [2] as an auxiliary method for
ensemble. The code is released at https://github.com/caiyuanhao1998/MST and
https://github.com/caiyuanhao1998/MST-plus-plus.

Implementation: Retinexformer is implemented by PyTorch. The model is trained
with the Adam [41] optimizer ($\beta_{1}$ = 0.9 and $\beta_{2}$ = 0.999) for
2.5 $\times$ 105 iterations. The learning rate is initially set to 2$\times
10^{-4}$ and then steadily decreased to 1$\times 10^{-6}$ by the cosine
annealing scheme [52] during the training process. Patches at the size of
2000$\times$2000 are randomly cropped from the low-/normal-light image pairs
as training samples. The batch size is 8. The training data is augmented with
random rotation and flipping. The training objective is to minimize the mean
absolute error (MAE) between the enhanced image and ground-truth image. The
team use the mixed-precision training strategy (also the amp in Pytorch).

In the testing phase, images with the size of 2000$\times$3000 are directly
fed into the network. Images with the size of 4000$\times$6000 are firstly
split into two 4000$\times$3000 images to undergo the model and then merged to
obtain the enhanced images. Self-ensemble and multi-model ensemble strategies
are used for final testing.

###  4.3 DH-AISP

Description: DWT-FFC [83] is employed as the backbone in our proposed method
for low-light image enhancement. Dark light usually results in degraded image
quality, low contrast, color shift, and structural distortion. We have
observed that many deep learning-based models show superior performance in
dark light enhancement, but they are generally not up to the challenge of
lower light. There are two main factors contributing to this. First, due to
the uneven distribution of image brightness, it is very challenging to recover
structure and chromaticity features with highfdelity, especially in areas of
extremely low brightness. Secondly, the existing small-scale data sets for
dark light enhancement are not sufcient to support reliable learning of
feature mapping based on convolutional neural network (CNN) models. To address
these two challenges, we employ a novel two-branch network utilizing two-
dimensional discrete Walsh transform (DWT), fast Fourier Convolution (FFC)
residual blocks, and a pre-trained ConvNeXt model. Specifcally, in the DWT-FFC
frequency branch, our model utilizes DWT to capture more high-frequency
features. In addition, by utilizing the large receptive feld provided by FFC
residuals, our model is able to efciently explore global context information
and generate images with better perceptual quality. In the prior knowledge
branch, we use the pre-trained ImageNet ConvNeXt instead of Res2Net. This
allows our model to learn more complementary information and gain greater
generalization ability. The feasibility and eﬀectiveness of the proposed
method are proved by extensive experiments.

Implementation: The training dataset is based solely on data provided by the
competition organizers. The training images are generated by random clipping,
and the training GPU is RTX 4080. A training session takes about 48 hours on a
single GPU. The optimizer uses Adam. The initial learning rate is 0.0001,
halved every 500 epochs.Training in python based on pytorch platform.

###  4.4 NWPU-DiffLight

Figure 3: DiffLight pipeline. DiffLight is composed of two branches: (a)
Denoising Enhancement (DE) branch and (b) Detail Preservation (DP) branch.

Description: As shown in Fig. 3, we proposed a dual-branch pipeline called
DiffLight. Besides, we proposed a method, Progressive Patch Fusion (PPF) for
high-resolution image restoration. Given a low-light image as input, it will
be fed into the two branches separately. In the DE branch (Fig. 3 (a) ), the
low-quality image is processed sequentially by DiffIR [5] and LEDNet [65] to
get the image enhanced by DE branch, $Output_{DE}$:

$\mathbf{Output}_{DE}=\text{LEDNet}(\text{DIffBackward}(\mathbf{Input})),$
(11)

where $\text{DiffBackward}(\cdot)$ represents the the Backward Process of
DiffIR (which is for inference). The enhanced image, $Output_{DE}$, has fairly
low noise, minimal color deviation, as well as highly-increased brightness,
but there is an excessive loss of details.

In the DP branch (Fig. 3 (b)), the newly proposed method for high-resolution
image restoration, PPF, is used. To better recover the details from high-
resolution images, the image is divided into $n$ small low-quality (LQ)
overlapping patches in the Segmentation process, the patch size is adapted to
the input size for training. Each patch is individually processed through the
Restormer to obtain $n$ high-quality (HQ) overlapping patches. To get
$Output_{DP}$, the image enhanced by DP-branch, PPF is applied on these
enhanced patches:

$\mathbf{Output}_{DP}=\text{PPF}(\text{Restormer}(\text{Segmentation}(\mathbf{Input}))).$
(12)

The block artifacts produced by the traditional fusion method is removed by
PPF, providing good visual quality and rich details. Finally, $Output_{DE}$
and $Output_{DP}$ are multiplied by weights $w_{1}$ and $w_{2}$ respectively,
and summed to form the final output image after weighted averaging:

$\mathbf{Output}=w_{1}\mathbf{Output}_{DE}+w_{2}\mathbf{Output}_{DP}.$ (13)

Progressive Patch Fusion. For high-resolution images, we utilize the
Progressive Patch Fusion (PPF) method during testing. This approach
incorporates progressive weight management at the to effectively mitigate edge
and substantially improve visual fidelity. PPF is performed by these steps as
follows:

  1. 1.

Image Segmentation: The input image $I$ is segmented into patches of size $p$.
These patches slide across the image with a stride ($s$) of stride to
determine their locations.

  2. 2.

Model Inference: The model is used to sequentially infer each patch, resulting
in inferred patches.

  3. 3.

Weight Calculation: Four weight tensors, $weight_{1,2,3,4}$ are computed for
blending overlapping regions. The weights for overlapping regions linearly
range from 1 to 0. $weight_{1,2}$ are used for blending overlaps between
patches, while $weight_{3,4}$ are used for merging overlaps between rows.

  4. 4.

Image Reconstruction: Initialize an empty restore image and $patch_{row}$ row
image, which will be used to store the reconstructed image and the current row
being processed.

  5. 5.

Fusion Process:

     * •

For each $h_{i}$ (row index) and $w_{i}$ (column index), extract the
corresponding patch from the patches tensor. If it is the first patch of the
row ($w_{i}$ equals $0$), add it directly to the $patch_{row}$.

     * •

For subsequent patches, use $weight_{1,2}$ to blend the overlapping regions
between the two patches.

     * •

Add the processed $patch_{row}$ to the restored image $I^{\prime}$.

     * •

If it is not the first row ($h_{i}\neq 0$), blending between rows using
$weight_{3,4}$ is required.

  6. 6.

Output: the aforementioned steps, restore tensor contains the the restored
image $I^{\prime}$.

Implementation:  For the Denoising Enhancement (DE) branch, we train DiffIR
[5] and LEDNet [65] separately. In training the diffusion model, total
timesteps $T$ are set to $4$, and $\beta_{t}$ linearly increase from
$\beta_{1}=0.9$ to $\beta_{T}=0.99$ . We train the two stage of DiffIR [5]
only using $\mathcal{L}_{1}$ loss. We train $\text{DiffIR}_{S1}$ for 300K
iterations with the initial learning rate $2\times 10^{-4}$ gradually reduced
with the cosine annealing. And For $\text{DiffIR}_{S2}$, we train 300K
iterations with initial learning rate $2\times 10^{-4}$ and gamma $0.5$ with
the MultiStepLR scheduler. For both training stage, we progressively increase
patch size and decrease batch size. Specifically, during iterative training,
the patch size and batch size pair are set to respectively train for $(92K)$,
$(80K)$, $(38K)$, $(90K)$ iterations under the configurations of $(192,8)$,
$(256,4)$, $(320,2)$, $(400,1)$.

LEDNet [65] model is trained on the inference results on Train set produced by
DiffIR, and the Ground Truth remains unchanged. We train LEDNet using Adam
[41] optimizer with $\beta_{1}=0.9$, $\beta_{2}=0.99$ for a total of 300k
iterations. The initial learning rate is set to $1\times 10^{-4}$ and updated
with cosine annealing strategy [52]. Similar to DiffIR, we still adopt a
progressive training approach, the patch size and batch size pair are set to
train for $(90K)$, $(70K)$, $(70K)$, $(70K)$ iterations respectively under the
configurations of $(256,8)$, $(512,4)$, $(1024,1)$, $(1320,1)$.

We implement our the Detail-Preservation (DP) branch by PyTorch. The model is
trained with the Adam [41] optimizer ($\beta_{1}$ = 0.9 and $\beta_{2}$ =
0.999) for at least 300 epochs by using a single NIVIDA 3090 GPU. The learning
rate is initially set to $1\times 10^{-4}$ and then steadily decreased to
$1\times 10^{-7}$ by the cosine annealing scheme [52] during the training
process. We randomly crop the image to 256 $\times$ 256 for patch size and set
batch size to $8$. When testing, we use our proposed Progressive Patch Fusion
(PPF) method that addresses edge artifacts and yields favorable visual
perceptual results with high resolution images.

###  4.5 GiantPandaCV

Figure 4: An overview of our UHDMamba for UHD Low Light Enhancement.

Description: Existing restoration backbones are usually limited due to the
inherent local reductive bias or quadratic computational complexity in Ultra-
High-Defnition (UHD) image restoration tasks. Recently, Selective Structured
State Space Model, _e.g._ , Mamba [50], has shown great potential for long-
range dependencies modeling with linear complexity. Therefore, we introduce
the Selective Structured State Space Model [33] into the low light image
enhancement task and propose an efficient model for UHD images, named
UHDMamba. Specifically, our UHDMamba uses the Residual State Space Block as
the core component, which employs convolution and channel attention to enhance
the capabilities of the vanilla Mamba. To efficiently infer UHD images, we
used PixelUnshuffle to downsample the UHD images and then input them into the
network for enhancement. Finally, we used PixelShuffle upsampling to
reconstruct the final image.

The main architecture of the UHDMamba is shown in Fig. 4, which consists of
three components: Shallow feature extraction, Deep feature enhancement, and
Upsampling reconstruction. Given a low-quality (LQ) input image
$I_{LQ}\in\mathcal{R}^{H\times W\times 3}$, we first employ a $3\times 3$
convolution layer from the shallow feature extraction to generate the shallow
feature $F_{s}\in\mathcal{R}^{H\times W\times\frac{C}{S}}$, where H and W
represent the height and width of the input image, C is the number of
channels, and S is the downsample scale factor. Subsequently, the shallow
feature $F_{s}$ undergoes downsample operation and the deep feature
enhancement stage to acquire the deep feature
$F_{d}^{l}\in\mathcal{R}^{\frac{H}{S}\times\frac{W}{S}\times SC}$ at the
$l-$th layer, $l\in{1,2,...,L}$. This stage is stacked by multiple Residual
State-Space Blocks (RSSBs). Finally, we use the element-wise sum to obtain the
input of the high-quality reconstruction stage $F_{R}=F_{L}+F_{s}$, which is
used to Upsample reconstruct the high-quality (HQ) output image $I_{HQ}$.

Implementation: The proposed method conducts experiments in PyTorch on two
NVIDIA GeForce RTX 3090 GPUs. To optimize the network, the model employs the
Adam [41] optimizer with a learning rate $2\times 10^{-4}$. We randomly crop
the full-resolution image to a resolution of $512\times 512$ as the input and
perform 200k iterations of training with a batch size of 4. To augment the
training data, random horizontal and vertical flips are applied to the input
images. The number of RSSB and feature channels is set to 8 and 48,
respectively. DownSampler and UpSampler are both composed of a sub-pixel
convolutional layer.

In order to maximize the potential performance of our model, the method adopts
the Test Time Augmentation strategy (TTA). During the test time, it used
$90^{\circ},180^{\circ},270^{\circ}$ rotation, horizontal flip, and vertical
flip to generate six augmented inputs
$\\{I_{n,i}^{input}=T_{i}(I_{n}^{input})\\}$ from input left and right images
$I_{n}^{{input}}$. With those augmented input images and the original input
image, we generate corresponding clear images
$\\{I_{n,1}^{output},\cdots,I_{n,6}^{output}\\}$ using the networks. We then
apply an inverse transform to those output images to get the original geometry
$\tilde{I}_{n,i}^{output}=T_{i}^{-1}(I_{n,i}^{output})$. Finally, we average
the transformed outputs altogether to make the TTA result as follows.
$I_{n}^{output}=\frac{1}{6}\sum_{i=1}^{6}{\tilde{I}_{n,i}^{output}}$.

###  4.6 LVGroup_HFUT

Figure 5: The network architecture of team LVGroup_HFUT.

Description: Low light enhancement refers to a process or set of techniques in
computer vision aimed at improving the visual quality of images by adjusting
their lighting conditions. This process can involve increasing the brightness
and contrast, correcting underexposed or overexposed areas, and enhancing
details in shadows and highlights. The goal is to produce images that more
accurately reflect the scene as perceived by the human eye, even under less-
than-ideal lighting conditions. However, complex models are difficult to
reason about on the GPU, limited by the larger image resolution. Therefore,
NAFNet [18] is chosed as baseline and further lighten NAFNet based on it to
get a brand new structure. Specifically, the number of channels is kept
constant at the early stage of the encoding phase and at the end of the
decoding phase, while the up- and down-sampling operations are performed, as
shown in Fig. 5.

More specifically, the input image is initially subjected to pixel alignment
and convolution operations, during which the number of channels is expanded
from 3 to 8. Subsequently, the image passes through an encoder and a
downsampling module, resulting in an increase in channel count to 288, while
its dimensionality is reduced to one-thirty-second of its original size.
Following this, a decoder accompanied by an upsampling module is applied,
which adjusts the channel count to 18 and restores the dimensions to their
original size. The final output is then obtained through a convolutional
layer. During the training process, the model is constrained using a
combination of lpips loss, fft loss, and L1 loss to optimize performance and
ensure fidelity to the input image.

Implementation: The proposed architecture is based on PyTorch 2.2.1 and an
NVIDIA 4090 with 24G memory. 2000 epochs are set for training with batch size
2, using AdamW with $\beta_{1}=0.9$ and $\beta_{2}=0.999$ for optimization.
The initial learning rate was set to 0.001, and cosine annealing was used for
learning rate adjustment. The randomly crop the image to 768×768 is first
performed and then horizontal flip with probability 0.5 is performed for data
augment. The input image is fed into the network, and it is constrained using
three loss functions: lpips loss with weight 0.5, fft loss with weight 0.1,
and $L_{1}$ loss with weight 1.

The testing process begins by combining a total of $10$ equally-spaced
checkpoints obtained in training phase. Specifically, the input image with
original resolution sequentially passes through these $10$ checkpoints,
resulting in $10$ outputs. Finally, the model ensemble strategy is applied to
process these $10$ outputs, yielding the ultimate output. Besides, since the
test set contains images with too large resolution ($4000\times 6000$), A100
(40G memory) is used for inference to avoid the ‘cuda out of memory’ error.

###  4.7 Try1try8

Figure 6: The network architecture of team Try1try8.

Description: Due to the large resolution of the inference images, the authors
added a downsampling operation to the RetinexFormer, renaming it to
RetinexFormerD. The authors employed a RetinexFormerD model architecture with
varying training patch sizes, and cascaded it with a denoising model (HatD)
and a fine-tuning model(RetinexFormerDseg). Finally, the authors performed a
model ensemble of all the model results. The model is shown in Fig. 6.

Implementation: path1: Initially, the authors applied downsampling to the
RetinexFormer to create RetinexFormerD (due to the large size of the inference
images), followed by the use of the HATD model (which incorporates the HAT
model with downsampling) to denoise the inference results of RetinexFormerD.
Ultimately, RetinexFormerSeg (which integrates image segmentation results into
the model) was utilized to fine-tune the final results. path2: The authors
trained RetinexFormerD with various patch sizes and stages, resulting in three
Retinex-FormerD models: RetinexFormerD ds2s1 with a patch size of 1024,
RetinexFormerD ds2s2 with a patch size of 768, and RetinexFormerD ds2s4 with a
patch size of 512. Finally, the authors averaged the results obtained from
path1 and path2. The model is trained with Adam optimizer, 2e-4 as learning
rate, and on V100

###  4.8 Pixel_warrior

Figure 7: The network architecture of team Pixel$\\_$warrior.

Description: The team proposes an efficient vision MLP-based architecture for
low-light enhancement (see Fig. 7). Our MLP block contains dimension
transformation operations (see Fig. 8). Specifically, we first normalize the
input features, and then perform multi-stage dimension transformations to
rotate the spatial perspective of tensors across 3 dimensions of $H$, $W$ and
$C$. Here, the 3D feature map undergoes recursive encoding from $(C,H,W)$ to
$(H,W,C)$ and then to $(W,C,H)$, enabling the capture of global spatial
information through multi-view dimensions. Finally, we adjust the feature map
to the original resolution, and interact with the input features to activate
useful features.

Implementation: We conduct network training on four NVIDIA Tesla V100 GPUs
with 32GB memory. In total, we perform 500 epochs of training. During the
training, we adopt the Adam optimizer with a learning rate of $2\times
10^{-4}$. The patch size is set to be $2000\times 2000$ pixels and the batch
size is set to be 4. To augment the training data, we apply random horizontal
and vertical flips.

Figure 8: The network architecture of the MLP block.

###  4.9 HuiT

Figure 9: Illustration of the propsed LLformer.(Re-produced from LLformer
[69])

Description: The team introduced LLformer [69] to address this issue. As shown
in Fig. 9, given a low-light image, LLformer first extracts shallow features
$f_{0}$, which are then fed into three sequential Transformer blocks to
extract deep features. Specifically, the intermediate features output from the
Transformer blocks are denoted as $f_{1}$, $f_{2}$, $f_{3}$. These features
are aggregated and transformed into enhanced image features $f_{4}$ using the
proposed cross-layer attention fusion block. Subsequently, deep feature
extraction is performed on $f_{4}$ using four stages in the encoder.
Specifically, each stage includes Patch Merging and Axis-Transformer blocks.
Then, the low-resolution latent features are gradually restored to a high-
resolution representation using the decoder, which consists of three stages
and takes $x_{3}$ as input. Each stage consists of Patch Expanding and Axis-
Transformer blocks. To reduce information loss at the encoding end and achieve
better feature restoration at the decoding end, weighted skip connections with
$1\times 1$ convolutions are used for feature fusion in both the encoding and
decoding ends. Thirdly, after decoding, the deep features f are sequentially
processed through three Axis-Transformer blocks and a cross-layer attention
fusion block to generate enhanced features for image reconstruction. Finally,
the enhanced image is produced. The Axis-Transformer block, as shown in Fig.
10, performs self-attention mechanism along the height and width axes of the
features in the cross-channel dimension to capture non-local self-similarity
and long-range correlations with lower computational complexity. The Cross-
Attention Fusion block, as shown in Fig. 11, learns attention weights between
different layer features and adaptively fuses features with the learned
weights to improve feature representation.

Figure 10: Illustration of the proposed Axis-Transformer Block.

Figure 11: Illustration of the proposed Cross-Attention Fusion Block.

Implementation: The code is implemented based on the Pytorch framework, We
trained the model in 300 epochs on two NVIDIA 4090 GPU with batchsize of $24$.
The model are optimized by the Adam with $\beta_{1}$ = $0.9$ and $\beta_{2}$ =
$0.999$ with weight decay 1e-8 by default. The initial learning rate was set
to 2$e$-4, and true cosine annealing was chosen as the learning scheme. The
loss function is defined as:

$\pounds_{total}=\pounds_{pix}+\lambda\pounds_{ssim},$ (14)

where $\pounds_{pix}$ represents the L1 Loss, and $\pounds_{ssim}$ signifies
the SSIM Loss. The parameter $\lambda$ denotes the loss weight used to balance
the influence between the two terms. In our experiments $\lambda$ is set to
$1.0$.

To save computational resources, high-definition images need to be chunked,
however, the size of the patches has a great impact on the results.
Experiments have shown that the best results will be achieved by segmenting
the original image of size $2000\times 2996$ into multiple patches of size
$500\times 748$, and by segmenting the original image of size $4000\times
6000$ into patches of size $800\times 1200$. We randomly cropped $128\times
128$ smaller patches from the processed images during the network training
process. Following the data augmentation techniques outlined in the LLFormer
[69] methodology, we applied random horizontal and vertical flips to these
patches.

###  4.10 X-LIME

Description: Our method consists of two modules, which are the curve
estimation network for lightness adaption and the denoising network for
denoising. Specifically, we adopt Zero-DCE [34] as a lightness adaption
network and NAFNet [19] as a denoising network. For better lightness adaption
performance, we adjust the relevant loss function. Specifically, we add
MSELoss to the original Zero-DCE [35] loss and train for 10 epochs on the
provided dataset. In the training phase, we use Zero-DCE pre-trained weights
provided in the official Github repository and fine-tune the provided dataset
for the first stage. For the second stage, we used NAFNet [19] pre-trained on
the SIDD dataset and fine-tuned the output of the lightness module. In the
testing phase, we first use the lightness adaption module to adapt the
lightness, thus increasing the contrast in the image. Then we feed the output
of the lightness module into the denoising module to further mitigate the
noise.

Implementation: Our proposed method is implemented in Python with the help of
the PyTorch framework. The models used in the two stages are optimized with
Adam. In the first stage, the learning rate is set to 1e-4 and in the second
stage, the learning rate is set to 5e-5, respectively. In the first stage, we
adapt the RTX4090 GPU while the A100 80G is used in the second stage. The
convergence speed of the first stage is rather fast, so we only train 10
epochs on the training set with the full image as input, resulting in a
30-minute training. For the second stage, we train the NAFNet [19] with a
progressively enlarged patch strategy. The first 30000 iterations are trained
with $512\times 512$ patches, then we enlarge the training patch to
$1024\times 1024$ for 25000 iterations. Finally, we use $1920\times 1920$
patches for 10000 iterations. During testing, we use TLC [21] to further boost
the performance of our patch-based method. The base size of TLC is set to
(1920, 2980).

###  4.11 Image Lab

Figure 12: Architecture Diagram of ImageLab Team’s Demosaicing Model

Description: We propose a novel image denoising and enhancement architecture,
leveraging the power of Coordinate Convolution (CoordConv) [46] layers and
Self-Calibrated Convolution with pixel attention (SCPA) blocks [85, 61], as
shown in Fig. 12. The architecture is designed to suppress noise while
effectively preserving important image details.

The input image is first passed through a CoordConv layer to add channels
containing hard-coded coordinates, enriching the representation with spatial
information. This augmented representation is then downsampled and fed into
five consecutive SCPA layers with a Pixel Attention Block. SCPA layers enhance
the model’s capture of intricate spatial patterns and features. The output
from the SCPA layers is upsampled and added back to the input image. This
combined representation is passed through a Modified U-Net architecture with
Residual Dense Channel Attention (RDCA) blocks [60]. The U-Net consists of
three encoder blocks and two decoder blocks. Each encoder block contains two
RDCA blocks followed by downsampling, while each decoder block contains two
RDCA blocks followed by upsampling. This design facilitates the extraction and
refinement of features at multiple scales. Simultaneously, the input image is
fed into a sophisticated denoising block [22] to produce a three-channel
denoised image. The denoising block comprises four inverse convolutional
layers followed by an attention mechanism. This mechanism effectively
suppresses noise while preserving important image details, enhancing the
overall quality of the denoised image. The outputs of the RDCA-UNet and the
denoising block are added to the input image, resulting in a final enhanced
image combining the denoising block’s denoised features with the refined
features from the RDCA-UNet.

Implementation: The proposed network was trained using the NVIDIA Tesla P100
with 16GB RAM and the TensorFlow Keras platform. 400x400x3 patches were
randomly extracted from the images. The training dataset consisted of 4281
patches, and 755 patches were used for validation. Augmentation techniques
were applied during training. The model was optimized using the Adam optimizer
with a learning rate schedule that decreased from 0.001 to 0.00001 over 250
epochs. The model comprises 0.437556 million parameters. The training
objective included a combination of $\mathcal{L}_{1}$, $\mathcal{L}_{SSIM}$,
and $\mathcal{L}_{Grad}$ loss, _i.e._ ,

$\mathcal{L}=0.1*\mathcal{L}_{SSIM}+\mathcal{L}_{1}+\mathcal{L}_{Grad}.$ (15)

###  4.12 dgzzqteam

Figure 13: InstructIR [23] takes as input an image and a human-written
instruction for how to improve that image. The multi-task model performs text-
guided low-light image enhancement.

Description: The author claimed that the solution is based on the published
OGF, while not providing the specific citation. The authors changed the loss
function and fine-tuned the model so that it could perform better in the
competition.

Implementation: The authors implement the OGF by PyTorch. The model is trained
with the Adam optimizers ($\beta_{1}$ = 0.9 and $\beta_{2}$ = 0.999) for 2.5
$\times$ 105 iterations. The learning rate is initially set to 2$e$-4 and then
steadily decreased to 1$e$-6 by the cosine annealing scheme during the
training process. Patches at the size of 128 $\times$ 128 are randomly cropped
from the low-/normallight image pairs as training samples. The batch size is
8. The training objective is to minimize the mean absolute error (MAE) between
the lit-up image and the enhanced image. The training and validation sets are
split in proportion to 209:21.

###  4.13 Cidaut AI

Description:  We use InstructIR [23] for real-world low-light enhancement.
InstructIR [23] takes as input an image and a human-written instruction for
how to improve that image. The neural model performs all-in-one image
restoration. InstructIR [23] achieves state-of-the-art results on several
restoration tasks including image denoising, deraining, deblurring, dehazing,
and (low-light) image enhancement. The approach is illustrated in Fig. 13.

The model achieves $23.07$ dB, $0.8075$ SSIM and $0.156$ LPIPS in the NTIRE
2024 Low-light Enhancement Challenge, _representing a baseline solution for
multi-task restoration using text-guidance._

Implementation: The model can process full-resolution images in a regular GPU
without tiling strategies. We fine-tune the model using the challenge dataset
besides LOL [71]. We use the instructions _“correct the low illumination in
this image”_ for all the test images.

Efficient Baseline Methods: The team also studies efficient methods that can
process full-resolution images in real-time at several FPS: RetinexNet [71],
SCI [55] and Zero-DCE [34]. All these methods can process $4000\times 6000$
directly on regular GPUs such as Nvidia 3090Ti. However, these methods did not
improve over InstructIR [23].

Datasets: To enhance our results and generalize better, we included other
training datasets: LOL-v2 (real and synthetic) and the MIT5K dataset. We also
apply random crop, flip and rotation augmentations.

###  4.14 OptDev

Figure 14: Overall framework the Team OptDev’s DFormer

Description: The authors developed a novel transformer-based deep network
DoubleFormer (DFormer) to learn low-light to highlight mapping, as shown in
Fig. 14. The architecture comprises two separate encoder-decoder blocks (EDB)
and a multi-head correlation block (MHCB) to produce plausible images. The
authors leverage illumination mapping from the well-known Retinex theory to
accelerate our reconstruction performance. Also, the authors incorporated the
state-of-the-art low-light enhancement method, Retinexformer in the overall
architecture’s first half. Regrettably, Retinexformer failed to generate
plausible images in many tricky scenes while illustrating visible noise and
color desaturation in complex regions. To address this limitation, the authors
proposed to utilize an MHCB, followed by another EDB in our architecture. We
leverage the correlated features with intermediate output in the second EDB to
perceive better denoising results. In addition to that, the authors utilized a
perceptual loss, including luminance-chrominance guidance, to address the
color inconsistency.

Implementation: The proposed solution is implemented with the PyTorch
framework. The networks were optimized with a Adam optimizer, where the
hyperparameters were tuned as $\beta_{1}$ = 0.9, $\beta_{2}$ = 0.99, and
learning rate = 5e-4. We trained our model with non-overlapping image patches
with a constant batch size of 12, which takes around 36 hours to complete. We
conducted our experiments on an NVIDIA A6000 graphical processing unit (GPU)
machine.

###  4.15 ataza

Figure 15: Overall framework our Team ataza’s Frequency Guided Network

Description: Team ataza proposes a low parameter efficient network, as shown
in Fig. 15. Based on the observation that error in low light images mainly
exist in the low-frequency region they decompose the image into high-frequency
and low-frequency subbands using Discrete Wavelet Transform (DWT) whose
utility for image processing has been well established [62]. Specifically,
they choose HAAR wavelet [56] due to it’s computational efficiency and
excellent ability to detect edges [58]. The model consists of two branches one
for high-frequency and the other for low-frequency features. This approach
allows the model to focus on recovering low-frequency information while
preserving high-frequency information that otherwise would have been lost in
the deeper layers.

In the low-frequency branch, three Low-Frequency Blocks (LF Blocks) handle and
extract low-frequency features. Subsequently, after each LF Block, a DWT block
re-divides the output, channeling the high-frequency features to the high-
frequency branch. Within the high-frequency branch, the existing high-
frequency features are fused with those received via the DWT block from the
low-frequency branch, before undergoing processing by the High-Frequency Block
(HF Block). A total of four HF Blocks are employed in this process. This
approach enables the model to preserve high-frequency features while
concentrating on the recovery of low-frequency information. Given that the
low-frequency subbands of the image have more errors, our approach involves
making the LF Block deeper and more complex compared to the HF Block.

The outputs of the three LF Blocks are concatenated, while the outputs of the
last three HF Blocks are also concatenated. Both sets of features are then
fused and the final image generated. The model is trained using two loss
functions: the $L_{1}$ loss and a frequency-domain-based loss $Loss_{FFT}$
derived from Fast Fourier Transform (FFT). $Loss_{FFT}$ is defined by Eq. 16,
and the overall loss is determined by Eq. 17. Incorporating a frequency-based
loss enhances model stability since the network is extracting features in the
frequency domain, _i.e._ ,

$Loss_{FFT}=\frac{1}{n}\sum_{i=1}^{n}||FFT(I_{g})|-|FFT(I_{t})||,$ (16)
$Loss=Loss_{FFT}+20*Loss_{L_{1}}.$ (17)

This approach enables the solution to possess only 387,414 parameters while
still achieving competitive performance. It can process a 1024x1024 image in
just 0.14s and a 512x512 image in 0.04s. The rapid speed, coupled with low
complexity, renders it suitable for various real-world applications where a
balance of performance and speed is crucial.

Implementation: The solution, developed in Python with the PyTorch framework,
was trained on an Intel i7-8700 CPU @ 3.20GHz, 16GB RAM, and NVIDIA GeForce
RTX 3070 graphics card. Data provided by the competition was exclusively used
for training the model. The training dataset comprised 230 pairs of images,
each sized 2992x2000 pixels. Out of these, 220 images were allocated for
training, with the remaining 10 reserved for validation. To augment the
dataset, non-overlapping patches of size 256x256 pixels were extracted from
each image and used to train the model. Training lasted 70 epochs, employing
the Cosine Annealing strategy with a cycle length of 10 epochs and an initial
learning rate set at 0.0005. To train the model Adam optimizer, in conjunction
with PyTorch’s mixed precision feature was used and batch size was set to 8.

###  4.16 KLETech-CEVI_LowlightHypnotise

Figure 16: Overview of the proposed Multi-scale Feature FusionNet (MFNN). The
encoder extracts features in three distinct scales, with information passed
across hierarchies (green dashed box). Fine-grained global-local spatial and
contextual information is learnt through the GLSC Block (orange dashed box).
At decoder, information exchange occurs in reverse hierarchies (blue dashed
box).

Description: The proposed MFNN framework includes three main modules: the
hierarchical spatio-contextual (HSC) feature encoder, Global-Local Spatio-
Contextual (GLSC) block, and hierarchical spatio-contextual (HSC) decoder, as
shown in Fig. 16. Typically, low-light image enhancement networks employ
feature scaling for varying the sizes of the receptive fields. The varying
receptive fields facilitate learning of local-to-global variances in the
features. With this motivation, we learn contextual information from multi-
scale features while preserving high resolution spatial details. We achieve
this via a hierarchical style encoder-decoder network with residual blocks as
the backbone for learning. Given a input image x, the proposed multi-scale
hierarchical encoder extracts shallow features in three distinct scales and is
given as:

$F_{si}=M_{Es}(x),$ (18)

where $F_{si}$ are the shallow features extracted at the $i^{th}$ scale from
the sampled space of input image $x$ and, $M_{Es}$ represents the hierarchical
encoder at scale $s$. To learn the global-to-local representations from these
shallow-level features, we propose a Global-Local Spatio-Contextual (GLSC)
block, with residual blocks as the backbone. The learnt deep features are
represented as:

$D_{si}=\text{GLSC}_{si}(F_{si}),$ (19)

where $D_{si}$ is the deep feature at the $i^{th}$ scale, $F_{si}$ are the
shallow features extracted at the $i^{th}$ scale and, $GLSC_{si}$ represents
residual blocks at respective scales. We decode the deep features obtained at
various scales, with the proposed hierarchical decoder and is given by:

$d_{si}=M_{dsi}(D_{si}),$ (20)

where $d_{si}$ is the decoded feature at the $i^{th}$ scale, and $M_{dsi}$
represents the hierarchical decoder. The decoded features and upscaled
features at each scale are passed to the reconstruction layers $M_{r}$ to
obtain the enhanced image ${\hat{y}}$. The upscaled features from each scale
are stacked and represented as:

$P=d_{s1}+d_{s2}+d_{s3},$ (21)

where $d_{s1}$, $d_{s2}$, and $d_{s3}$ are decoded features at three distinct
scales, $P$ represents the final set of features passed to reconstruction
layers to obtain the enhanced image ${\hat{y}}$:

$\hat{y}=M_{r}(P),$ (22)

where $\hat{y}$ is the enhanced image obtained from reconstruction layers
$M_{r}$. We optimize the learning of MFNN with the proposed
${\mathcal{L}_{MFNN}}$ and is given as:

$\mathcal{L}_{MFNN}=\alpha*L_{1}+\beta*\mathcal{L}_{VGG}+\gamma*\mathcal{L}_{MSSSIM},$
(23)

where $\alpha$, $\beta$, and $\gamma$ are the weights. We experimentally set
the weights to $\alpha=0.5$, $\beta=0.7$, and $\gamma=0.5$.
${\mathcal{L}_{MFNN}}$ is a weighted combination of three distinct losses
inspired from [25, 36, 26, 27]. $L_{1}$ loss to minimize error at pixel level,
perceptual loss [43] to efficiently restore contextual information between the
ground-truth image and the output image, multiscale structural dissimilarity
loss to restore structural details. The aim here is to minimize the weighted
combinational loss ${\mathcal{L}_{MFNN}}$ given as:

$L(\theta)=\frac{1}{N}\sum_{i=1}^{N}\parallel
MFNN(x_{i}-y_{i})\parallel_{\mathcal{L}_{MFNN}},$ (24)

where $\theta$ denotes the learnable parameters of the proposed framework, $N$
is the total number of training pairs, $x$ and $y$ are the input and output
image respectively, and $MFNN(\cdot)$ is proposed framework for low-light
image enhancement. Implementation: We train the proposed MFNN using Python
(v3.8) coupled with PyTorch framework and conduct experiments on 2x NVIDIA RTX
3090 GPUs with AMD Ryzen ThreadRipper 3960X CPU. We use 16 residual blocks
split into three layers of the hierarchy, and generate 256 feature maps for
each scale. We train the proposed MFNN on dataset provided in the challenge
along with LoL Dataset [71] with patches of size 600*400. During training, we
use Adam optimizer with $\beta_{1}=0.9$, $\beta_{2}=0.999$,
$\epsilon=10e^{-8}$, and set the learning rate $lr=0.0002$. We train the
proposed MFNN for 1000 epochs. We optimize the learning with proposed
$\mathcal{L}_{MFNN}$. During testing, we use full resolution images
($2992\times 2000$), on single RTX 3090 GPU. Average testing time for single
image on full resolution is 0.53s on RTX 3090 GPU.

###  4.17 221B

Figure 17: Overview of diffusion model training process with the
differentiable spatial entropy loss proposed by the team 221B.

The organizer wants to highlight that this method has obtained the first place
on the LPIPS ranking, with a score of 0.1084, outperforming the overall best-
ranked work with a score of 0.1221.

Description: Most existing diffusion-based works [37] simply employ
$\ell_{1}$/$\ell_{2}$ loss to train the noise prediction network in a
deterministic way, which always produces smooth intermediate noises (or
states) in the reverse process. Although the perceptual quality can be
improved with longer diffusion steps, it is reasonable to explore a
perceptual-based loss function facilitate the training process. In this work,
they propose to learn the network from a pure statistic perspective, that is,
directly learning distributions rather than pixel-by-pixel distances. The core
idea is the differentiable spatial entropy that drived from the kernel density
estimation (KDE) and measures the spatial information of an image. Formally,
following [63, 3], the two-dimensional KDE is defined as the probability of a
specific intensity value $i$ and its neighborhood $j$ as:

$\begin{split}\@add@centering\centering\displaystyle\hat{f}_{h}(i,j)&=\frac{2}{Nh}\sum_{x\in
X}\frac{1}{2}\mathbb{I}\left\\{\frac{|x-i|}{h}<1\right\\}\cdot\frac{1}{2}\mathbb{I}\left\\{\frac{|\tilde{x}-j|}{h}<1\right\\}\\\
&=\frac{2}{Nh}\sum_{x\in
X}\mathcal{K}_{1}(\frac{x-i}{h})\cdot\mathcal{K}_{2}(\frac{\tilde{x}-j}{h}),\end{split}$
(25)

where $\mathcal{K}_{1}$ and $\mathcal{K}_{2}$ are arbitrary kernel functions
(they can also be the same). To make use of the differentiable spatial KDE in
image generation, this work considers to introduce the relative entropy (also
called KL-divergence) to measure the distribution distance between the
predicted image and ground truth. Let $P$ and $Q$ represent the ground truth’s
and prediction’s probability distributions computed from Eq. 25, the spatial
relative entropy is defined to be

$\centering\displaystyle
H_{s}(P,Q)=D_{KL}(P||Q)=-\sum_{i=0}^{L}\sum_{j\in\Omega}p_{i,j}\log\frac{q_{i,j}}{p_{i,j}}.\@add@centering$
(26)

Their diffusion model is built on the mean-reverting SDE [53, 54]. By
optimizing the entropy-based distribution distance between predictions and
ground truth states, their results have better visual qualities than that only
using $\ell_{1}$/$\ell_{2}$ loss. Fig. 17 illustrates the overview of the
proposed diffusion training process.

Implementation: This team use the provided low-light dataset but further split
10 image pairs for validation. The Refusion framework is used as the baseline
and the team directly changed its loss funcion to the proposed differentiable
spatial entropy. In training, all images are cropped with patch size
$128\times 128$, and the total training iteration is set to 500,000 with a
batch 16. Following Refusion [54], this work use the AdamW [51] optimizer and
the ‘CosineDecay’ scheduler with an initial learning rate 0.0001. And the
diffusion steps are fixed to 100 in both training and testing.

###  4.18 KLETech-CEVI_Dark_Knights

Figure 18: Framework of Self-Calibrated Illumination Module (Reproduced from
[55]). We employ variant of this method for enhancement of low-light images
with custom loss function.

Description: In accordance with the Retinex theory, there exists a
relationship between the observed low-light image, denoted as $y$, and the
desired clear image $z$, expressed as $y=z\otimes x$, where $x$ signifies the
illumination component. Typically, optimization efforts in low-light image
enhancement primarily focus on refining the illumination component. To achieve
enhanced output, one common approach involves removing the estimated
illumination in accordance with the Retinex theory. Drawing inspiration from
prior works on illumination optimization, particularly those employing a
stage-wise approach, we propose a progressive perspective for modeling this
task. Here, we introduce a mapping $H_{\theta}$ parameterized by $\theta$ to
learn the illumination component, where each stage $t$ (with $t=0,...,T-1$)
consists of a basic unit represented as $u_{t}=H_{\theta}(x_{t})$ and
$x_{t+1}=x_{t}+u_{t}$, with $x_{0}=y$. It’s worth noting that we adopt a
weight sharing mechanism across stages, utilizing the same architecture $H$
and weights $\theta$ for each stage. Specifically, the parameterized operator
$H_{\theta}$ effectively learns a simple residual representation $u_{t}$
between the illumination and the observed low-light image. This process is
motivated by the consensus that illumination and low-light observations
exhibit similarities or linear connections in most areas.

We establish a module to ensure the convergence of results from each stage to
a consistent state. Given that the input of each stage originates from the
previous stage, and the initial input of the first stage is predetermined as
the low-light observation, we propose to indirectly explore convergence
behavior by bridging the input of each stage (excluding the first) with the
low-light observation. To achieve this, we employ a self-calibrated module
[55] denoted as $s$, which is added to the low-light observation to represent
the disparity between the input of each stage and the initial stage.
Specifically, the self-calibrated module, represented as $s=K_{\theta}(z)$,
utilizes the parameterized operator $K_{\theta}$ with learnable parameters
$\theta$ to generate $v=y+s$, where $v_{t}$ denotes the converted input for
each stage ($t\geq 1$). Consequently, the conversion for the basic unit in the
$t^{th}$ stage ($t\geq 1$) can be expressed as $F(x_{t})\rightarrow
F(G(x_{t}))$. This self-calibrated module effectively corrects the input of
each stage by integrating physical principles, thereby indirectly influencing
the output of each stage. In addition to this, we employ NIQE (Natural Image
Quality Evaluator) [59] as a loss function. NIQE offers a valuable approach
for improving perceptual quality. By utilizing NIQE as a metric to quantify
the naturalness and perceptual fidelity of enhanced images, the training
process aims to minimize the NIQE scores of the generated images. This
strategy encourages the model to produce output images that closely resemble
natural, high-quality images, enhancing their visual appeal and usability. By
optimizing for lower NIQE scores, the model learns to prioritize perceptually
pleasing enhancements, ultimately leading to outputs that are not only
visually appealing but also retain important natural characteristics.
Integrating NIQE as a loss function thus contributes to the development of
low-light image enhancement techniques that align closely with human
perception and preferences. NIQE as a loss function is given as:

$L_{\text{NIQE}}=\frac{1}{N}\sum_{i=1}^{N}\text{NIQE}(F(G(x_{t,i}))),$ (27)

where $N$ is the number of samples, $x_{t,i}$ represents the input to the
$t$-th stage for the $i$-th sample, $F(G(x_{t,i}))$ denotes the output of the
$t$-th stage for the $i$-th sample after applying the self-calibrated module,
and NIQE is the function that calculates the NIQE score for an image.
Minimizing this loss function during training would encourage the model to
produce output images with lower NIQE scores, indicating higher perceptual
quality and naturalness. This would result in output images that are closer in
quality to natural, high-quality images.
Implementation: We train the proposed method using Python (v3.8) coupled with
PyTorch framework and conduct experiments on 2x NVIDIA RTX 3090 GPUs with AMD
Ryzen ThreadRipper 3960X CPU. We use 16 residual blocks split into three
layers of the hierarchy, and generate 256 feature maps for each scale. We
train the proposed method on dataset provided in the challenge along with LoL
Dataset [71] with images of size 2992*2000. During training, we use Adam
optimizer with $\beta_{1}=0.9$, $\beta_{2}=0.999$, $\epsilon=10e^{-8}$, and
set the learning rate $lr=0.0002$. We train the proposed method for 1000
epochs. During testing, we use full resolution images (2992 * 2000), on single
RTX 3090 GPU. Average testing time for single image on full resolution is
0.53s on RTX 3090 GPU.

###  4.19 BFU-LL

Figure 19: The network architecture of team BFU-LL.

Description: Diverging from the use of more complex, Transformer-based models
for image restoration [32, 30], this team focuses on addressing low-light
issues through the application of diffusion methods. They introduce a novel
approach for enhancing low-light images proposed by [40], as illustrated in
Fig. 19. The method first converting these images into the wavelet domain via
2D discrete wavelet transformation (DWT), iterated multiple times. This
process yields an average coefficient and sets of high-frequency coefficients.
By employing Haar wavelets [29], the method divides the input into four sub-
bands, effectively capturing global information and sparse local details. This
significantly reduces spatial dimensions while preserving essential image
information. The team’s wavelet-based diffusion model utilizes a forward
diffusion process during the training phase and a denoising process across
both training and inference phases. Moreover, they have developed a High-
Frequency Restoration Module (HFRM) for the reconstruction of high-frequency
details. Utilizing the U-Net architecture as the noise estimator network and
implementing a fixed variance schedule, their method methodically transforms
the input into corrupted noise data, subsequently employing Gaussian denoising
transitions to achieve a clear and enhanced output.

Building on the methodologies described in previous studies [76, 28], this
team design and adopt a hybrid loss function as follows:

$\mathcal{L}_{\text{total}}=\mathcal{L}_{\text{smooth}}+\alpha\mathcal{L}_{\text{MS-
SSIM}}+\beta\mathcal{L}_{\text{per}},$ (28)

where $\alpha=0.01$, $\beta=0.01$, and $\gamma=0.005$ are the hyperparameters
weighting each loss component.

Implementation: The implementation by this team is conducted using PyTorch.
The proposed network effectively converges after undergoing training for
$1\times 10^{4}$ iterations on a system equipped with four NVIDIA RTX 1080Ti
GPUs. For optimization, the Adam optimizer is employed. Considering the impact
of the learning rate on experimental results [31], the initial learning rate
is set to $1\times 10^{-4}$. This learning rate decays by a factor of 0.8
after every $5\times 10^{3}$ iterations. The team utilizes a batch size of 12
and a patch size of $256\times 256$. The scale $K$ for the wavelet
transformation is set to 2.

###  4.20 SVNIT_NTNU

(a)

(b)

Figure 20: The architecture of the proposed model for Low Light Enhancement.

Description: In order to design low light image enhancement,we use multiple
Retinex blocks and multiple channel attention in the proposed solution. The
Fig. 20(a) depicts the proposed architecture for Low Light enhancement. The
initial step involves feeding the low-light image into a denoiser module
tailored to address the noise typical of low-exposure conditions.
Subsequently, a shallow feature extraction process, achieved through a single
convolutional operation, projects the denoised image into a feature space.

Following this, the image undergoes enhancement through multi-branch Adaptive
Retinex (AR) Blocks operating at various scales. These blocks simultaneously
enhance both broad and fine details within the image.

The shallow features and the enhanced results are then merged within a
multiple channel attention module, which effectively extracts global and local
features and combines them using a sigmoid function.

Lastly, the fused features are refined through another convolutional layer,
culminating in the production of a clearer, brighter rendition of the original
low-light image. Despite the addition of the channel attention module, which
marginally increases parameter count, the overall network remains lightweight,
owing to the efficiency achieved through the AR Blocks and denoiser modules.

The network incorporates multiple Adaptive Retinex Blocks (ARBlocks), which
represent a novel extension of the Single Scale Retinex method in feature
space [9]. At the core of each ARBlock lies an efficient illumination
estimation function known as the Adaptive Surround Function (ASF) [82]. This
function, akin to a general form of surround functions, is implemented using
convolutional layers. The ARBlock serves a dual purpose, facilitating both
illumination adjustment and reflectance enhancement within the image. Fig. 20
illustrates the architecture of the ARBlock, demonstrating its capability to
enhance both low-frequency and high-frequency components of the image,
respectively.

Implementation: The code is implemented using Pytorch library. The proposed
network is trained using weighted combination of $l_{1}$, SSIM loss, and
charbornnier loss with a learning rate of $1\times 10^{-4}$ which is decayed
by $1\times 10^{4}$ iterations and the same is optimized using Adam optimizer.
The model is trained up to $1\times 10^{5}$ iterations with a batch size of
$4$.

###  4.21 yanhailong

Figure 21: General method flow chart.

Description: We propose ZSRADNet: Zero-Shot Retinex Joint Adaptive-
Illumination and Denoising for Underexposed Image Enhancement, which mainly
considers underexposed images often suffer severe quality degradation. Many
scenes exhibit issues related to occlusion and shadows, resulting in non-
uniform illumination in the images. Most methods for restoring underexposed
images rely on global enhancement, which can lead to excessive enhancement in
areas that were originally well-lit. Both supervised and unsupervised learning
approaches have limitations: either poor generalization or susceptibility to
unstable training. To address these challenges, the proposed a Zero-Shot
method that learns enhancement solely from test images. It consists of two
main components: The Decomposition Network and The Denoising Network.

As shown in Fig. 21, inspired by [84], the Decomposition network employs a
dual-branch structure to break down the input image into three components:
Illumination, Reflection, and Noise. Initially, the input image passes through
a simple four-layer CNN (Convolutional Neural Network) combined with ReLU
(Rectified Linear Unit) activation and a Sigmoid function to obtain the
illumination. Next, a designed DenseBlock layer, combined with ReLU and
Convolution, generates the reflection (using Sigmoid activation) and the noise
(using Tanh activation). DenseBlock can make better use of the feature
information of the previous layer and help to improve feature reusability.

To achieve Zero-Shot enhancement, the proposed combines loss functions of
Retinex reconstruction, Reflectance smoothness, and Illumination-guided noise
estimation. By iteratively minimizing this combined loss function, the
proposed effectively estimates the noise and restores the illumination.
Additionally, the proposed introduces an adaptive gamma correction method that
uses illumination as a measure of non-uniformly lit regions in the image. It
brightens underexposed areas while leaving well-lit regions untouched. Our
loss function consists of three parts:

$L=L_{recon}+{\lambda_{ref}}\cdot{L_{ref}}+{\lambda_{noise}}\cdot{L_{noise}},$
(29)

where $L_{recon}$, $L_{ref}$ and $L_{noise}$ represent reconstruction loss,
reflectance smoothness loss, and Illumination-guided noise loss respectively,
and $\lambda_{ref}$, $\lambda_{noise}$ represents corresponding weight
factors.

The Denoising network generates a pair of noise maps [57] from a single noise
image and utilizes these maps to train a simple three-layer network (CNN
combined with SMU activation [6]). Specifically, the proposed begins by
applying a down-sampling operator to decompose the output from the
Decomposition network into a pair of down-sampled images. One of these
observations serves as the input value, while the other acts as the target.
The proposed then employs regularization techniques during training to achieve
effective denoising.

As a method specific to a single input image, it does not require any prior
image samples or prior training models. The final enhanced results have
advantages in the brightness and naturalness of non-uniform illumination
images.

Implementation: The team uses Pytorch and the test device is the A6000 GPU.
The illumination branch of the Decomposition network comprises a four-layer
structure consisting of $3\times 3$ Convolution followed by ReLU activation
functions. On the other hand, the reflection and noise branches utilize a
four-layer architecture of $3\times 3$ Convolution with Denseblock, followed
by ReLU activation functions, culminating in an additional layer of $3\times
3$ Convolution. The network undergoes a total of $1000$ iterations during
training. In the Denoising network, two layers of $3\times 3$ Convolution are
employed, each followed by SMU activation, and ultimately connected to a final
layer of $3\times 3$ Convolution. The training process utilizes Mean Squared
Error (MSE) loss is used for $2000$ iterations. All the learning rates were
$0.01$; the optimizer uses Adam; without training, the input of a single image
directly outputs the enhanced result.

###  4.22 Mishka

Figure 22: Illustrative diagram showing the application of Retinexformer for
low-light enhancement.

Description: As shown in Fig. 22, we employed the Retinexformer [8] model, a
one-stage Retinex-based transformer, specifically designed for low-light image
enhancement tasks. Our approach is distinguished by training the Retinexformer
model directly on the dataset provided by the competition, demonstrating its
adaptability and generalization capability across different datasets. This
direct training method underscores the model’s effectiveness and flexibility
for specific low-light conditions. Our method maintains a moderate level of
complexity throughout all stages, ensuring efficiency and effectiveness from
data preprocessing to model training and final evaluation.

Implementation: Our training configuration details various aspects of the
model training process. We conducted a total of 300,000 iterations without a
warm-up phase, and gradient clipping was employed to prevent gradient
explosion. During training, we divided the 300,000 iterations into two cycles:

  * •

The first cycle involved a fixed learning rate of 3$e$-4 for the initial
92,000 iterations.

  * •

The second cycle applied a cosine annealing strategy, decreasing the learning
rate from 3$e$-4 to 1$e$-6 over the subsequent 208,000 iterations.

We also utilized mixed data augmentation techniques , and for the optimizer
configuration, we chose the Adam optimizer with an initial learning rate of
2$e$-4 and betas set to [0.9, 0.999]. Regarding the loss function, we employed
the $L_{1}$ Loss, with a loss weight of 1 and reduction method set to mean.

## Acknowledgements

This work was partially supported by the Humboldt Foundation. We thank the
NTIRE 2024 sponsors: Meta Reality Labs, OPPO, KuaiShou, Huawei and University
of Würzburg (Computer Vision Lab).

##  Appendix A Teams and affiliations

### NTIRE 2024 team

Title:  NTIRE 2024 Low Light Enhancement Challenge
Members:
Xiaoning Liu1 (liuxiaoning2016@sina.com),
Zongwei Wu2 (zongwei.wu@uni-wuerzburg.de),
Ao Li1 (aoli@std.uestc.edu.cn),
Florin Vasluianu2 (florin-alexandru.vasluianu@uni-wuerzburg.de),
Yulun Zhang3 (yulun100@gmail.com),
Shuhang Gu1 (shuhanggu@gmail.com),
Le Zhang1 (lezhang@uestc.edu.cn),
Ce Zhu1 (eczhu@uestc.edu.cn),
Radu Timofte2,4<EMAIL_ADDRESS>
Affiliations:
1 University of Electronic Science and Technology of China, China
2 Computer Vision Lab, University of Würzburg, Germany
3 Shanghai Jiao Tong University, China
4 Computer Vision Lab, ETH Zurich, Switzerland

### SYSU-FVL-T2

Title: Scale-Robust Low-light Ultra-High-Definition Image Enhancement
Members:
Zhi Jin1,2 (jinzh26@mail.sysu.edu.cn), Hongjun Wu1, Chenxi Wang1, Haitao Ling1
Affiliations:
1 School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen
University, Shenzhen, Guangdong 518107, China
2 Guangdong Provincial Key Laboratory of Fire Science and Technology,
Guangzhou 510006, China

### Retinexformer

Title:  Retinexformer: One-stage Retinex-based Transformer for Low-light Image
Enhancement
Members:
Yuanhao Cai1 (caiyuanhao1998@gmail.com), Hao Bian2, Yuxin Zheng2, Jing Lin2,
Alan Yuille1
Affiliations:
1 Johns Hopkins University
2 Tsinghua University

### DH-AISP

Title:  Multi-scale feature fusion Low-light Image Enhancement
Members:
Ben Shao1 (2830802483@qq.com), Jin Guo1, Tianli Liu1, Mohao Wu1
Affiliations:
1 Zhejiang Dahua Technology Co.,Ltd.

### NWPU-DiffLight

Title:  DiffLight: Integrating Content and Detail for Low-light Image
Enhancement
Members:
Yixu Feng1 (yixu-nwpu@mail.nwpu.edu.cn), Shuo Hou1, Haotian Lin2, Yu Zhu1,
Peng Wu1, Wei Dong3, Jinqiu Sun1, Yanning Zhang1, Qingsen Yan1
Affiliations:
1 School of Computer Science, Northwestern Polytechnical University, China
2 School of Software, Northwestern Polytechnical University, China 3 School of
Computer Science, Xi’an University of Architecture and Technology, China

### GiantPandaCV

Title:  UHDMamba: Towards Effective and Efficient State-Space Model for UHD
Low-light Image Enhancement
Members:
Wenbin Zou1 (alexzou14@foxmail.com), Weipeng Yang1, Yunxiang Li2, Qiaomu Wei3,
Tian Ye4, Sixiang Chen3,4
Affiliations:
1 South China University of Technology, China
2 Fuzhou University, China
3 Chengdu University of Information Technology, China
4 Hong Kong University of Science and Technology (Guangzhou), China

### LVGroup_HFUT

Title:  Exploring the Application of NAFNet in Low Light Enhancement
Members:
Zhao Zhang1 (cszzhang@gmail.com), Suiyi Zhao1, Bo Wang1, Yan Luo1, Zhichao
Zuo1, Mingshen Wang1, Junhu Wang1, Yanyan Wei1
Affiliations:
1 Hefei University of Technology, China

### Try1try8

Title:  Low Enhancement Model Ensemble of Multi-patch RetinexFormers
Members:
Xiaopeng Sun1 (xpsun@stu.xidian.edu.cn), Yu Gao1 , Jiancheng Huang1
Affiliations:
1 Individual Researcher

### Pixel_warrior

Title:  MLP for Low-Light Image Enhancement
members:
Hongming Chen1 (chenhongming@stu.sau.edu.cn), Xiang Chen2
Affiliations:
1 Shenyang Aerospace University, China
2 Nanjing University of Science and Technology, China

### HuiT

Title:  Enhanced Low-Light Imaging Method Based on Axis-Transformer
Members:
Hui Tang1 (2639442956@qq.com), Yuanbin Chen1, Yuanbo Zhou1, Xinwei Dai1,
Xintao Qiu1, Wei Deng2, Qinquan Gao1,2, Tong Tong1,2
Affiliations:
1 Fuzhou University, Fuzhou, China
2 Imperial Vision Technology, Fuzhou, China

### X-LIME

Title:  Supervised Deep Curve Estimation with Denoiser Members:
Mingjia Li1 (mingjiali@tju.edu.cn), Jin Hu1, Xinyu He1, Xiaojie Guo1
Affiliations:
1 Tianjin University, China

### Image Lab

Title:  Enhancing Low-Light Images: A SCPA-Based Approach with RDCA-UNet
Architecture
Members:
Sabarinathan1 (sabarinathantce@gmail.com), K Uma2, A Sasithradevi3, B Sathya
Bama4, S. Mohamed Mansoor Roomi4, V.Srivatsav5
Affiliations:
1 Couger Inc,Japan
2 Sasi Institute of Technology & Engineering, India
3 Vellore Institute of Technology, India
4 Thiagarajar college of engineering, India
5 Coventry University, United Kingdom

### dgzzqteam

Title:  One-stage gatebased Framework
Members:
Jinjuan Wang (15600951607@163.com)1, Long Sun1, Qiuying Chen1, Jiahong Shao1,
Yizhi Zhang1
Affiliations:
1 Independent Researchers

### Cidaut AI

Title:  InstructIR: High-Quality Image Restoration Following Human
Instructions
https://github.com/mv-lab/InstructIR
Members:
Marcos V. Conde1,2 (marcos.conde@uni-wuerzburg.de), Daniel Feijoo1, Juan C.
Benito1, Alvaro García1
Affiliations:
1 Cidaut AI
2 CVLab, University of Wuerzburg

### OptDev

Title:  Learning Optimized Low-Light Image Enhancement for Edge Vision Tasks
Members:
Jaeho Lee1 (jaeho.lee@opt-ai.kr), Seongwan Kim1, Sharif S M A1, Nodirkhuja
Khujaev1, Roman Tsoy1
Affiliations:
1 Opt-AI

### ataza

Title:  An Efficient Frequency Guided Image Enhancement Network for Low Light
Image Enhancement and Shadow Removal
Members:
Ali Murtaza1,2 (ali.murtaza.ali29@outlook.com),
Uswah Khairuddin1,2, Ahmad ’Athif Mohd Faudzi2
Affiliations:
1 Malaysia-Japan International Institute of Technology (MJIIT), University
Teknologi Malaysia, Kuala Lumpur, , Malaysia
2 Center for Artificial Intelligence and Robotics (CAIRO), Universiti
Teknologi Malaysia, Kuala Lumpur, Malaysia

### KLETech-CEVI_LowlightHypnotise

Title: Hierarchical LightNet: Towards Low-Light Image Enhancement
Members:
Sampada Malagi1,3 (sampadamalagi12@gmail.com), Amogh Joshi1, Nikhil
Akalwadi1,3, Chaitra Desai1,3, Ramesh Ashok Tabib1,2, Uma Mudenagudi1,2
Affiliations:
1 Center of Excellence in Visual Intelligence (CEVI), KLE Technological
University, Hubballi, Karnataka, India
2 School of Electronics and Communication Engineering, KLE Technological
University, Hubballi, Karnataka, India
3 School of Computer Science and Engineering, KLE Technological University,
Hubballi, Karnataka, India

### 221B

Title:  Equipping Diffusion Models with Differentiable Spatial Entropy for
Low-Light Image Enhancement
Members:
Wenyi Lian1 (shermanlian@163.com), Wenjing Lian2
Affiliations:
1 Uppsala University
2 Northeastern University

### KLETech-CEVI_Dark_Knights

Title: KnightNet: Illumination Guided Low Light Image Enhancement Network
Members:
Jagadeesh Kalyanshetti1,3 (01fe21bcs004@kletech.ac.in), Vijayalaxmi Ashok
Aralikatti1,3, Palani Yashaswini1,2, Nitish Upasi1, Dikshit Hegde1, Ujwala
Patil1,2, Sujata C1,3
Affiliations:
1 Center of Excellence in Visual Intelligence (CEVI), KLE Technological
University, Hubballi, Karnataka, India
2 School of Electronics and Communication Engineering, KLE Technological
University, Hubballi, Karnataka, India
3 School of Computer Science and Engineering, KLE Technological University,
Hubballi, Karnataka, India

### BFU-LL

Title:  A Diffusion Model for Low-Light Enhancement
Members:
Xingzhuo Yan1 (ayx1sgh@bosch.com), Wei Hao2, Minghan Fu3
Affiliations:
1 Bosch Investment Ltd.
2 Fortinet, Inc.
3 University of Saskatchewan

### SVNIT_NTNU

Title: Retinex based Low Light Image Enhancement
Members:
Pooja choksy1 (ds22ec003@eced.svnit.ac.in), Anjali Sarvaiya1, Kishor Upla1,
Kiran Raja2

Affiliations:
1 Sardar Vallabhbhai National Institute of Technology, India
2 Norwegian University of Science and Technology, Norway

### yanhailong

Title: ZSRADNet: Zero-Shot Retinex Joint Adaptive-Illumination and Denoising
for Underexposed Image Enhancement
Members:
Hailong Yan<EMAIL_ADDRESS>
Affiliations:
University of Electronic Science and Technology of China, China

### Mishka

Title: Application and Adaptation of Retinexformer Members:
Yunkai Zhang1 (1585832651@qq.com), Baiang Li1, Jingyi Zhang1, Huan Zheng2
Affiliations:
1 Hefei University of Technology, China
2 University of Macau, China

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