# BLNet: A Fast Deep Learning Framework for Low-Light Image Enhancement with
Noise Removal and Color Restoration

Xinxu Wei, Xianshi Zhang1, Shisen Wang, Cheng Cheng, Yanlin Huang, Kaifu Yang,
and Yongjie Li This work was supported by Guangdong Key R&D Project
(#2018B030338001) and Natural Science Foundations of China (#61806041,
#62076055). (Corresponding author: Xian-Shi Zhang, email:
zhangxianshi@uestc.edu.cn)Xinxu Wei, Xianshi Zhang, Cheng Cheng, Yanlin Huang,
Kaifu Yang, Yongjie Li are with the MOE Key Lab for Neuroinformation,
University of Electronic Science and Technology of China (UESTC), Chengdu
610054, China.

###### Abstract

Images obtained in real-world low-light conditions are not only low in
brightness, but they also suffer from many other types of degradation, such as
color bias, unknown noise, detail loss and halo artifacts. In this paper, we
propose a very fast deep learning framework called Bringing the Lightness
(denoted as BLNet) that consists of two U-Nets with a series of well-designed
loss functions to tackle all of the above degradations. Based on Retinex
Theory, the decomposition net in our model can decompose low-light images into
reflectance and illumination and remove noise in the reflectance during the
decomposition phase. We propose a Noise and Color Bias Control module (NCBC
Module) that contains a convolutional neural network and two loss functions
(noise loss and color loss). This module is only used to calculate the loss
functions during the training phase, so our method is very fast during the
test phase. This module can smooth the reflectance to achieve the purpose of
noise removal while preserving details and edge information and controlling
color bias. We propose a network that can be trained to learn the mapping
between low-light and normal-light illumination and enhance the brightness of
images taken in low-light illumination. We train and evaluate the performance
of our proposed model over the real-world Low-Light (LOL) dataset), and we
also test our model over several other frequently used datasets (LIME, DICM
and MEF datasets). We conduct extensive experiments to demonstrate that our
approach achieves a promising effect with good rubustness and generalization
and outperforms many other state-of-the-art methods qualitatively and
quantitatively. Our method achieves high speed because we use loss functions
instead of introducing additional denoisers for noise removal and color
correction. The code and model are available at
https://github.com/weixinxu666/BLNet.

###### Index Terms:

Low-light image enhancement, Retinex decomposition, Image denoising, Color
bias control, Deep learning

##  I Introduction

Low-light image enhancement is a very challenging low-level computer vision
task because, while enhancing the brightness, we also need to control the
color bias, suppress amplified noise, preserve details and texture information
and restore blurred edges. Images captured in insufficient lighting conditions
often suffer from several types of degradation, such as poor visibility, low
contrast, color distortion and severe ISO noise, which have negative effects
on other computer vision tasks, such as image recognition [1] [2] [3], object
detection [4] [5] [6] and image segmentation [7] [8] [9]. Therefore, there is
a huge demand for low-light image enhancement. According to [10], although
adjusting camera settings (e.g., increasing ISO, extending exposure time and
using flash) can enhance the brightness of the image and improve the
visibility, it can also bring about specific problems, making the image suffer
from degradation to varying degrees. For example, increasing the ISO may
introduce additional noise and cause parts of the image to be overexposed.
Extending the exposure time may blur the objects in the image. Using flash
enhances the brightness of captured images, but it may also lead to an
unnatural image with color bias and uneven brightness. In recent years, a
great number of approaches have been proposed and achieved remarkable results
in low-light image enhancement, but, to the best of our knowledge, there are
few successful methods for simultaneously dealing with all degradations
contained in low-light images (such as low brightness, color bias, noise
pollution, detail and texture loss, edge blurring, halo artifacts and contrast
distortion).
It is still a very challenging task to cope with so many problems
simultaneously. Although many existing methods can improve the brightness of
low-light images, some of them cause serious color distortion after increasing
the brightness [11][12][13][14][15][10]. In addition, some methods do not
suppress noise when enhancing brightness, resulting in serious noise in the
image after enhancement [11][16][17]. Other methods [18][19][20], in order to
eliminate noise, cause the enhanced and denoised image to be too smooth,
resulting in blurred edges, missing details and texture information and color
bias. For example, KinD [10] uses a simple U-Net to decompose low-light
images. Noise hidden in the dark regions is amplified in reflectance, so a
deeper U-Net is used in KinD to remove noise in reflectance, but this
introduces color bias. Degradation can affect not only our visual effects but
other visual tasks as well. Therefore, how to control the color bias during
denoising is a problem worth studying. Based on Retinex Theory [21], a natural
image can be decomposed into two parts: reflectance and illumination. The
reflectance part contains details, texture and color information of the
original image. The illumination part carries information about the intensity
and distribution of light in the original image, and it should be smooth
enough and not contain any high-frequency information such as details and
texture. The decomposition task concentrates both the high-frequency
components and the noise on the reflectance part, whereas the illumination
part is mainly composed of low-frequency components, so there is almost no
noise in the illumination part and we only need to denoise the reflectance
part. Many previous methods do not consider denoising the enhanced image, such
as MSRCR [11], GLADNet [14] and EnlightenGan [15]. Some methods use other
state-of-the-art denoising methods as post-processing operations for enhanced
images, such as BM3D [22], DnCNN [23] and CBDNet [24], which can slow down the
speed of the model considerably. In other methods, a denoising network or
algorithm is specially designed to make the enhanced image noise-free, such as
KinD [10], which also slows down the speed of the model significantly. In this
paper, we improve RetinexNet [13] and propose a novel framework based on
Retinex Theory [21] to achieve brightness enhancement, denoising, color bias
correction and detail and texture preservation simultaneously and efficiently.
The proposed framework consists of two U-Nets [25] and a convolutional neural
network (CNN). In the decomposition phase, we use a U-Net to decompose low-
light images into reflectance and illumination. According to [26], the up-and-
down sampling structure of U-Net can be used as a decomposition network.
However, the main problem with using U-Net as the decomposition network is
that the decomposition results are accompanied by color distortion and
inconsistency. In addition, a large amount of noise will appear in reflectance
after decomposition because the noise is originally hidden in the dark regions
of the low-light image. We propose a Noise and Color Bias Control Module
(denoted as NCBC Module), which consists of a CNN and two loss functions,
noise loss and color loss, to suppress the noise and correct the color
distortion. Different from previous methods, instead of designing a network
specifically to deal with noise, we suppress the noise when conducting
decomposition and enhancement to avoid noise amplification. We also use color
loss to constrain the color information of the image and recover the correct
color from the dark regions, while also removing noise and enhancing
brightness. In the enhancement phase, we use a U-Net to reconstruct the
illumination and a series of loss functions to constrain the supervised
training process. We highlight the contributions of this paper as follows:

  * •

Based on the Retinex theory, we improve RetinexNet [13] and propose a very
fast deep-learning framework consisting of two U-Nets for low-light image
decomposition and enhancement, which can simultaneously address the problems
of low brightness, color bias, amplified noise, detail and texture loss,
blurred edges and distorted contrast.

  * •

We propose a Noise and Color Bias Control Module consisting of a convolutional
neural network. This module preserves the color information while smoothing
the noise in the reflectance, and it reconstructs the high-frequency
information.

  * •

Benefiting from the constraints of a series of well-adjusted loss functions,
in the enhancement phase, we propose a very fast enhancement network to
enhance the brightness of illumination in an image-to-image translation way.

  * •

Extensive comparisons and ablation experiments are conducted on commonly used
datasets to demonstrate the superiority of our method qualitatively and
quantitatively, including its fast computation speed.

##  II Related Works

In recent years, many effective methods have been developed for the tasks of
low-light image enhancement and image denoising.

###  II-A Low-Light Image Enhancement Methods.

According to the algorithm principle, low-light image enhancement methods can
be roughly grouped into two categories: traditional methods and deep learning
methods.

####  II-A1 Traditional Enhancement Methods

In traditional methods for low-light image enhancement, histogram equalization
(HE)-based approaches are a major category. There are many improved methods
based on HE. Dynamic histogram equalization (DHE) divides the histogram of the
image into sub-blocks and uses HE to stretch the contrast for each sub-block.
Adaptive histogram equalization (AHE) [27] changes image contrast by
calculating the histogram of multiple local areas of the image and
redistributing the brightness. AHE is more suitable for improving the local
contrast and details of the image, but the noise is amplified. Contrast
limited adaptive histogram equalization (CLAHE) [28] restricts the histogram
of each sub-block and well controls the noise brought by AHE. Brightness
preserving dynamic histogram equalization (BPDHE) [29] preserves the same
brightness between the enhanced image and the pre-enhanced image. DHECI [30]
uses differential gray-level histograms for color-image enhancement.
Methods based on Retinex Theory [21] decompose the image into reflectance and
illumination. Such methods maintain the consistency of the reflectance,
increase the brightness of the illumination and then take the pixel-wise
product to enhance the low-light image. Single Scale Retinex (SSR) [31] aims
to restore the brightness of illumination after Retinex decomposition. Multi-
Scale Retinex (MSR) [11] combines the filtering results of multiple scales
based on SSR. MSRCR adds a color recovery factor to tackle the color
distortion caused by contrast enhancement in local areas of the image. NPE
[32] strikes a balance between contrast enhancement and naturalness of
estimated illumination. MF [33] is a fashion-based method for contrast
enhancement of the illumination. SRIE [34] is a weighted variational model to
estimate reflectance and illumination. BIMEF [35] provides a bio-inspired
dual-exposure fusion algorithm to provide accurate contrast and lightness
enhancement, which obtains results with less contrast and lightness
distortion. LIME [12] estimates a structure-aware illumination map with
structure prior and uses BM3D [22] for the post-processing denoising
operation. Dong at el. [36] proposed an efficient algorithm by inverting an
input low-lighting video and applying an optimized image de-haze algorithm on
the inverted video. CRM [37] uses the camera response model for the
enhancement process. RRM [19] is a Robust Retinex Model that considers noise
mapping to improve the enhancement performance of low-light intensity noise
images. LECARM [38] is a novel enhancement framework using the response
characteristics of cameras to tackle color and lightness distortions. JED [20]
is a joint low-light enhancement and denoising strategy. DIE [39] is an
automatic exposure correction solution, which can produce high-quality results
for images of various exposure conditions. STAR [40] gives a novel structure
and texture aware Retinex model. LR3M [41] is a low-rank regularized retinex
model which can suppress noise in the reflectance map. PRIEN [42] proposes a
progressive-recursive image enhancement network.

####  II-A2 Deep Learning Based Enhancement Methods

With the advent of deep learning, a great number of state-of-the-art methods
have been developed for low-light image enhancement. LLNet [43] is a deep
auto-encoder model for enhancing lightness and denoising simultaneously. LLCNN
[44] is a CNN-based method utilizing multi-scale feature maps and SSIM loss
for low-light image enhancement. MSR-net [45] is a feedforward CNN with
different Gaussian convolution kernels to simulate the pipeline of MSR for
directly learning end-to-end mapping between dark and bright images. GLADNet
[14] is a global illumination-aware and detail-preserving network that
calculates global illumination estimation. LightenNet [14] serves as a
trainable CNN by taking a weakly illuminated image as the input and outputting
its illumination map. MBLLEN [18] uses multiple subnets for enhancement and
generates the output image through multi-branch fusion. RetinexNet [13]
decomposes low-light input into reflectance and illumination and enhances the
lightness over illumination. EnlightenGan [15] trains an unsupervised
generative adversarial network (GAN) without low/normal-light pairs. KinD [10]
first decomposes low-light images into a noisy reflectance and a smooth
illumination and then uses a U-Net to recover reflectance from noise and color
bias. RDGAN [16] proposes a Retinex decomposition based GAN for low-light
image enhancement. SID [46] uses a U-Net to enhance the extremely dark RAW
image. RetinexDIP [47] provides a unified deep framework using a novel
”generative” strategy for Retinex decomposition. Zhang et al. [48] presented a
self-supervised low-light image enhancement network, which is only trained
with low-light images. Zero-DCE [17] estimates the brightness curve of the
input image without any paired or unpaired data during training.

##  III Motivation

Based on the assumption of Retinex Theory [21], a natural image (S) can be
decomposed into two components: reflectance (R) and illumination (L).

$S=R*L$ (1)

where * represents a pixel-wise product operator. Reflectance is usually a
three-channel image that contains color and the most of the high-frequency
components, such as details and texture information. Illumination is a very
smooth single-channel image that only contains low-frequency components, such
as the intensity and distribution of lumination. In the process of
decomposition, illumination is smooth enough to be regarded as noise-free
since it only contains low-frequency information. However, noise hidden in the
dark is amplified in the reflectance, which results in a very low peak signal-
to-noise ratio (PSNR) for the reflectance. Previous approaches, such as LIME
[12], RetinexNet [13] and KinD[10], used additional well-designed denoisers,
such as BM3D [22], CBDNet [24] or an embedded denoiser, to denoise the
reflectance. However, there may be some problems such as color bias and loss
of high-frequency details in the reflectance after applying extra denoisers.
Furthermore, additional denoisers can significantly reduce the forward
inferencing speed of the whole pipeline. In the enhancement phase, the
brightness of the illumination is enhanced, but if the intensity of brightness
and the distribution of light are not restored correctly, the result will be
overexposed or underexposed. The color information of the image depends not
only on the reflectance but also on the brightness information of the
illumination. Incorrectly predicted illumination maps can also result in color
bias. A variety of degradations may arise after enhancing the brightness of
the low-light image. Many of the previous methods used multiple sub-methods or
sub-networks to tackle some of these problems in numerous steps [12][13][10],
which can slow down the speed of the pipeline. Unlike previous methods, we aim
to enhance the lightness of low-light images without introducing extra
networks to deal with real-world noise and color distortion. We use two simple
but effective U-Nets and the NCBC Module by carefully adjusting a series of
well-designed loss functions rather than designing multiple sub-networks to
deal with these problems individually. We are inspired by RetinexNet [13] and
we improve the shortcomings of RetinexNet.

Figure 2: The network architecture of our BLNet. Both the Decomposition Net
and Enhancement Net adopt the U-Net structure. Except for the difference in
the number of channels in their input and output, the structure and parameters
settings of the intermediate network are the same. The NCBC Module is a CNN
that consists of several stacked convolutional blocks, and the middle three
convolutional layers are weight sharing.

##  IV Methodology

As shown in Fig. 2, inspired by Retinex Theory, our proposed method adopts two
U-Nets as decomposition and enhancement networks. In the decomposition phase,
we use a U-Net to decompose the low-light image into two components (i.e.,
reflectance and illumination) and introduce a CNN as the Noise and Color Bias
Control Module (NCBC Module) for suppressing the noise in reflectance while
controlling color bias. In the enhancement phase, we also use a U-Net as our
enhancement net, whose kernel settings and network structure are nearly the
same as the first one, except for the number of channels of input and output
images. By carefully adjusting the well-designed loss functions, we can use
only these two U-Nets to enhance brightness, remove noise, restore color and
preserve details simultaneously. In the following sections, we will explain
our motivation, network architecture and loss functions in detail.

###  IV-A Decomposition Network

RetinexNet [13] uses a plain CNN as the decomposition net to decouple the low-
light image into the reflectance and illumination, but this CNN can amplify
the noise and introduce color distortion. KinD [10] uses a shallow U-Net as
the decomposition net, but the noise is still amplified in the reflectance and
there is also a serious color distortion. Therefore, inspired by [10], we use
a deeper U-Net as our decomposition net because the up-and-down sampling
structure of U-Net has the function of denoising and will keep the noise from
being amplified as much as possible. However, unpleasant noise and color
distortion still exist in the reflectance, so we introduce the NCBC Module to
tackle the noise and color bias in the reflectance. In the decomposition,
inspired by RetinexNet [13] and KinD [10], we also use the reconstruction loss
$L_{rc}$ proposed in RetinexNet [13] to constrain the process of
decomposition.

$L_{rc}=\sum_{i}\sum_{j}\lambda_{ij}\left\|R_{i}\ast L_{j}-S_{j}\right\|_{1}$
(2)

where $*$ denotes the pixel-wise product and $i,j$ denote the low- and high-
frequency components, respectively. $R_{high}$, $R_{low}$ and $L_{high}$,
$L_{low}$ represent the reflectance and the illumination decomposed by
GroundTruth and low-light input, respectively. When $i=j$, $\lambda_{ij}=1$;
otherwise, $\lambda_{ij}=0.001$.
We use the equal loss $L_{equal}$ proposed in [13] to constrain the
reflectance to be as similar as possible to that decomposed by GroundTruth.

$L_{equal}=\left\|R_{low}-R_{high}\right\|_{1}$ (3)

Previous Retinex-based methods [13][10] use the smoothness loss to maintain
the spatial smoothness of illumination so that the illumination contains only
low-frequency information, such as intensity and distribution of brightness,
and all of the high-frequency information, such as details and texture, are
retained in the reflectance map. We also use the smoothness loss $L_{smooth}$
proposed in RetinexNet [13] to keep our illumination spatially smooth.

$L_{smooth}=\sum_{i}\left\|\triangledown_{h}L_{i}\ast
e^{-\lambda\ast\triangledown_{h}R_{i}]}\right\|+\sum_{i}\left\|\triangledown_{v}L_{i}\ast
e^{-\lambda\ast\triangledown_{v}R_{i}]}\right\|$ (4)

where $i=low,high$ and $\triangledown_{h}$, $\triangledown_{v}$ denote the
gradients in the horizontal and vertical directions. According to RetinexNet
[13], we also set the weight coefficient $\lambda$ to 10.
The total decomposition loss is as follows:

$L_{decom}=1.0L_{rc}+0.1L_{smooth}+0.01L_{equal}$ (5)

According to the previous methods [12][13][16][10], the illumination map is
smooth enough and contains only low-frequency information, and all of the
noise after decomposition appears in the reflectance map. However, in our
opinion, the illumination map can contain some high-frequency details and
texture information, which can be restored by our enhancement net.

###  IV-B Noise and Color Bias Control Module

Through experiments, we found that directly using TV loss to smooth the
reflectance is not good for denoising and may lead to image distortion. In
order to denoise the reflectance map without introducing additional networks
or denoisers, which may slow down the processing of the pipeline and cause
color distortion in the reflectance map, we propose the NCBC Module to
suppress the noise and control the color bias of the reflectance. At the same
time, the details smoothed out by the TV loss along with the noise in the
reflectance are transferred and reconstructed into the illumination.
Therefore, unlike other methods, as shown in Fig. LABEL:ablation_ncbc, our
illumination also contains some high-frequency information such as details and
texture. In this way, we can save the details in the illumination map, which
means that the details can be restored and reconstructed by the enhancement
net. This allows us to filter out the noise in the reflectance map while
preserving the details and texture information.
We use a stacked CNN to extract the feature map of the reflectance as the
estimated noise map, which contains noise level and color distribution
information of the reflectance. The color and texture of the image belong to
global information. Even though the convolution operation is a local operator,
we can construct a CNN by stacking multiple convolutional layers to enlarge
the receptive field so as to obtain the global feature information of the
image, such as color and texture. Hence, the output of our NCBC Module has the
ability to capture the feature map of global noise level and color information
of the image.
Our NCBC Module consists of a plain CNN, whose architecture is shown in Fig.
2, and two loss functions: noise loss and color loss. The input images of our
NCBC Module are: (1) the reflectance with noise and color distortion, which is
decomposed from the low-light input images and the reflectance without noise;
and (2) the color distortion, which is decomposed from normal-light
GroundTruth. We apply TV loss to the output of the reflectance with noise and
color distortion to smooth the $R_{low}$. Total variance loss (TV loss) [24]
is as follows:

$L_{TV}^{low}=\left\|\triangledown_{h}\phi(R_{low})]\right\|_{2}^{2}+\left\|\triangledown_{v}\phi(R_{low})]\right\|_{2}^{2}$
(6)

where $\phi$ means the output feature map of our NCBC Module, and
$\triangledown_{h}$ and $\triangledown_{v}$ denote the gradients in the
horizontal and vertical directions. The noise level of GroundTruth is zero by
default. According to [49], we use MSE loss to denoise and constrain the
similarity of $R_{low}$ and $R_{high}$ while using TV loss to smooth the
reflectance.

$L_{MSE}=\left\|R_{low}-R_{high}\right\|_{2}^{2}$ (7)

So our noise loss is as follows:

$L_{noise}=0.05L_{TV}^{low}+1.0L_{MSE}$ (8)

In addition, we use color loss $L_{color}$ to recover the reflectance from the
color distortion caused by the decomposition U-Net in the process of
decomposition, and we apply TV loss on the reflectance map of low-light images
for denoising. Inspired by low-lightGan [50], the color loss is as follows:

$L_{color}=\left\|\phi(R_{low})-\phi(R_{high})\right\|_{1}$ (9)

where $\phi$ is the output feature map of our NCBC Module. Low-lightGan [50]
uses a Gaussian blur function to remove local details of images while
preserving global information, such as color and texture, and then it adopts
color loss to alleviate the color distortion problem by forcing the global
information of the two images to be as close as possible.
The total NCBC loss is as follows:

$L_{NCBC}=0.2L_{noise}+0.1L_{color}$ (10)

###  IV-C Enhancement Network

The network structure and kernel settings of our enhancement network are the
same as those of the decomposition network. In particular, we use a series of
loss functions for the brightness enhancement in order to deal with noise
removal, color restoration and detail and texture preservation simultaneously.
The effectiveness of our method depends primarily on the contribution of our
enhancement loss function. The enhancement loss function in the enhancement
phase is as follows:

$L_{eh}=1.0L_{rc}+1.0L_{bri}+1.0L_{per}+1.0L_{grad}$ (11)

We use reconstruction loss $L_{rc}$ to constrain the final output after the
pixel-wise product to be as close as possible to the GroundTruth. The
reconstruction loss is as follows:

$L_{rc}=\left\|R_{low}\ast L_{output}-S_{high}\right\|_{1}$ (12)

where $R_{low}$, $L_{output}$ and $S_{high}$ respectively represent the
reflectance decomposed from the low-light image, the brightness-improved
illumination by our enhancement net and the normal-light input GroundTruth.
We use $L1$ loss as our brighten loss function $L_{bri}$ to enhance the
brightness of the illumination decomposed from low-light images and constrain
the output brightness-improved illumination to be as similar as possible to
its counterpart from GroundTruth. The brighten loss function is as follows:

$L_{bri}=\left\|L_{output}-L_{high}\right\|_{1}$ (13)

where $L_{output}$ and $L_{high}$ denote the brightness-improved output of the
enhancement net and the decomposed illumination of the normal-light
GroundTruth, respectively.
Perceptual loss is widely used in super-resolution and style tranfer [51].
Because we transfer some of the image details from reflectance to
illumination, we use perceptual loss to preserve the texture information
during illumination reconstruction in the enhancement network. In addition,
perceptual loss can make our output as similar to the normal-light GroundTruth
as possible in perception. Perceptual loss $L_{per}$ is as follows:

$L_{per}=\frac{1}{CHW}\left\|\phi(R_{low}\ast
L_{output})-\phi(S_{high})\right\|_{2}^{2}$ (14)

where $\phi$ is the 31st feature map obtained by the VGG16 [2] network pre-
trained on ImageNet database. C,H,W represents the number of channels, the
height and the width of the input image, respectively.
We find that the obtained output is rather rough, and the details and edges
information are still lost partly. Therefore, we use gradient loss $L_{grad}$
to maintain the balance of the sharpness and smoothness of the output.

$\displaystyle L_{grad}=\left\|\triangledown_{h}(R_{low}\ast
L_{output})-\triangledown_{h}(S_{high})\right\|_{1}$ (15)
$\displaystyle+\left\|\triangledown_{v}(R_{low}\ast
L_{output})-\triangledown_{v}(S_{high})\right\|_{1}$

where $\triangledown_{h}$, $\triangledown_{v}$ denote the gradients in the
horizontal and vertical directions, respectively. $S_{high}$ is the normal-
light GroundTruth.
The channel number of the enhancement net is four. Like RetinexNet [13], we
also concatenate the illumination and reflectance of the low-light image
decomposed by the decomposition net as the four-channel input of the
enhancement net. Because both the illumination and the reflectance contain
high-frequency details and texture information after decomposition,
concatenating the illumination and reflectance as the input of our enhancement
net can help the enhancement net reconstruct the details and texture
information. In addition, the output of the enhancement net is the
corresponding brightness-improved illumination with a single channel. Finally,
we multiply the output illuminantion with the noise-removed reflectance at the
pixel-wise level and obtain the final output, which is noise-free, detail-
preserved, color-restored and brightness-improved.

##  V Experimental Evaluation

###  V-A Implementation details

We train our model on the LOL [13] training dataset and evaluate it on the LOL
validation dataset. In addition, we test our model on three popular test
datasets: DICM [52] dataset, LIME [12] dataset and MEF [53] dataset. The LOL
dataset consists of two parts: the real-world dataset and the synthetic
dataset. Because the LOL synthetic dataset cannot simulate the degradation of
real-world images very well, we deprecate the synthetic dataset where low-
light images are synthesized from normal-light images and only use the real-
world dataset, which contains 500 low-light/normal-light image pairs captured
by adjusting the exposure time and ISO of the camera. We use 485 image pairs
as the training set and the remaining 15 image pairs as the validation set. We
use the Adam [54] optimizer to optimize the training of the model and set the
training batch-size to four and the patch-size of random crop to 320x320. We
use the PyTorch framework to build our model on a PC with an Nvidia TITAN XP
GPU and an Intel Core i7-9700 3.00GHz CPU.
We evaluate our method on the LOL [13] validation dataset and test it on
several widely used datasets, including DICM [52] dataset, LIME [12] dataset
and MEF [53] dataset. We adopt PSNR, SSIM [55], LPIPS [56], FSIM [57] and UQI
[58] as the quantative metrics to measure the performance of our method. In
addition, we use Angular Error [59] and DeltaE[60] as the metrics of color
distortion to calculate the color bias between our results and GroundTruth.
The Angular Error is as follows:

$AngularError=arcos(\frac{<S_{output},S_{high}>}{\left\|S_{output}\right\|\cdot\left\|S_{high}\right\|})$
(16)

where $S_{output}$ and $S_{high}$ denote the final output of our method and
the normal-light input GroundTruth, respectively.

###  V-B Ablation Study

As shown in Fig. LABEL:abla1, obvious noise and serious color bias exist in
the results generated by RetinexNet [13]. Our model without the NCBC Module is
able to enhance the brightness well but cannot deal with noise, color bias and
unpleasant artifacts. As shown in Fig. LABEL:abla2, the results generated by
our method without the NCBC Module are relatively low in terms of PSNR and
SSIM, with slight color distortion and unpleasant artifacts. In contrast, our
model with the NCBC Module can not only enhance the brightness but also
suppress noise, correct color bias and eliminate halo artifacts well. As shown
in Fig. LABEL:ablation_ncbc, when we remove the NCBC Module from the training
of the decomposition network, the result is full of noise, artifacts and color
distortion. In contrast, when we add the NCBC Module and use the noise loss to
suppress noise, most of the noise in the reflectance is removed. By increasing
the coefficient of noise loss, we can increase the degree of noise suppression
in the reflectance. The greater the coefficient of noise loss we set, the
better the denoising effect we can achieve. We set the coefficient of noise
loss to 0.2, which enables our model to achieve the best results in noise
reduction and color bias correction.

TABLE I: Quantitative comparison of several metrics between our method and other state-of-the-art methods on LOL dataset. “↑” indicates the higher the better, “↓” indicates the lower the better. Red: the best, Blue: the second best. Traditional Methods | PSNR↑ | SSIM↑ | LPIPS↓ | FSIM↑ | UQI↑
---|---|---|---|---|---
Input | 7.7733 | 0.1914 | 0.4173 | 0.7190 | 0.0622
MSRCR [11] | 13.1728 | 0.4615 | 0.4404 | 0.8450 | 0.7884
BIMEF [35] | 13.8752 | 0.5949 | 0.3673 | 0.9263 | 0.7088
LIME [12] | 16.7586 | 0.4449 | 0.4183 | 0.8549 | 0.8805
Dong [36] | 16.7165 | 0.4783 | 0.4226 | 0.8886 | 0.8078
SRIE [34] | 11.8552 | 0.4954 | 0.3657 | 0.9085 | 0.5033
MF [33] | 16.9662 | 0.5075 | 0.4092 | 0.9236 | 0.8572
NPE [32] | 16.9697 | 0.4839 | 0.4156 | 0.8964 | 0.8943
RRM [19] | 13.8765 | 0.6636 | 0.3476 | 0.8821 | 0.7275
LECARM [38] | 14.4099 | 0.5448 | 0.3687 | 0.9288 | 0.6406
JED [20] | 13.6857 | 0.6509 | 0.3549 | 0.8812 | 0.7143
PLM [61] | 16.2620 | 0.4617 | 0.4284 | 0.8265 | 0.8892
DIE [39] | 14.0181 | 0.5188 | 0.3910 | 0.9172 | 0.7027
DL Methods | PSNR↑ | SSIM↑ | LPIPS↓ | FSIM↑ | UQI↑
MBLLEN [18] | 17.8583 | 0.7247 | 0.3672 | 0.9262 | 0.8261
RetinexNet [13] | 16.7740 | 0.4249 | 0.4670 | 0.8642 | 0.9110
GLAD [14] | 19.7182 | 0.6820 | 0.3994 | 0.9329 | 0.9204
RDGAN [16] | 15.9363 | 0.6357 | 0.3985 | 0.9276 | 0.8296
Zero-DCE [17] | 14.8671 | 0.5623 | 0.3852 | 0.9276 | 0.7205
Zhang [48] | 19.4968 | 0.7003 | 0.3911 | 0.8514 | 0.8521
EnlightenGan [15] | 17.4828 | 0.6515 | 0.3903 | 0.9226 | 0.8499
Our model w/o NCBC | 18.4446 | 0.7605 | 0.3514 | 0.9261 | 0.9259
Our model | 20.1447 | 0.7918 | 0.3126 | 0.9454 | 0.9371
TABLE II: Quantitative comparison of color distortion between our method and other state-of-the-art methods on LOL dataset.“↓” indicates the lower the better. Red: the best, Blue: the second best. Traditional Methods | Mean↓ | Median↓ | Average↓ | DeltaE↓
---|---|---|---|---
MSRCR [11] | 3.7421 | 4.5877 | 4.1649 | 27.4496
BIMEF [35] | 3.4004 | 3.5187 | 3.4595 | 33.8820
LIME [12] | 3.2096 | 4.0825 | 3.6460 | 21.1816
Dong [36] | 3.3499 | 4.1481 | 3.7490 | 25.3349
SRIE [34] | 3.4488 | 4.0751 | 3.7620 | 44.3194
MF [33] | 3.3277 | 3.9810 | 3.6544 | 24.5488
NPE [32] | 3.5588 | 4.2505 | 3.9046 | 22.6374
RRM [19] | 3.3745 | 3.5821 | 3.4784 | 32.9843
LECARM [38] | 3.4091 | 3.9668 | 3.6979 | 34.5143
JED [20] | 3.4064 | 3.8651 | 3.6357 | 33.8342
PLM [61] | 3.4274 | 3.7085 | 3.4829 | 22.0553
DIE [39] | 3.4597 | 4.1164 | 3.7880 | 34.1583
Deep Learning Methods | Mean↓ | Median↓ | Average↓ | DeltaE↓
MBLLEN [18] | 3.2716 | 4.4620 | 3.8669 | 21.5774
RetinexNet [13] | 3.7501 | 4.4975 | 4.3589 | 21.3550
GLAD [14] | 3.3110 | 3.8021 | 3.5565 | 16.0393
RDGAN [16] | 4.3899 | 5.3027 | 4.8463 | 26.3796
Zero-DCE [17] | 4.1051 | 4.6860 | 4.3955 | 31.4451
Zhang [48] | 3.3744 | 3.9104 | 3.6424 | 17.6652
EnlightenGan [15] | 4.5296 | 5.2536 | 4.8916 | 21.9113
Our model w/o NCBC | 2.8645 | 3.9083 | 3.3864 | 16.0604
Our model | 2.1785 | 2.3870 | 2.3024 | 13.5264
TABLE III: Quantitative comparison of the entropy and inference speed between our method and other state-of-the-art deep learning methods on LOL dataset.“↓” indicates the lower the better. Red: the best, Blue: the second best. *All of the deep learning models are run on a PC with Nvidia TiTan XP GPU. Deep Learning Methods | GE↑ | CE↑ | Time cost↓
---|---|---|---
Low-Light Input | 4.7432 | 14.3837 | -
GroundTruth | 7.0400 | 21.3163 | -
MBLLEN [18] | 7.1421 | 21.3664 | 80 ms
RetinexNet [13] | 6.8346 | 21.1266 | 20 ms
GLAD [14] | 7.1141 | 21.5237 | 25 ms
RDGAN [16] | 6.6327 | 20.1053 | 30 ms
Zero-DCE [17] | 6.5964 | 18.9072 | 2 ms
Zhang [48] | 7.0673 | 21.4085 | 20 ms
EnlightenGan [15] | 7.0664 | 21.3476 | 20 ms
Our model w/o NCBC | 7.0336 | 21.3651 | 7 ms
Our model | 7.1450 | 21.5882 | 7 ms

###  V-C Visual Performance Analysis

As shown in Fig. LABEL:court and Fig. LABEL:book, the low-light images from
the LOL dataset are real-world images with very low brightness. The images
enhanced by other methods contain serious noise pollution and color bias, low
brightness and local over-exposure and under-exposure degradations. In
particular, for RetinexNet[13], there is so much amplified noise and color
deviation in the images that they no longer look like real-world images. In
contrast, our method can effectively solve these problems. It can be seen that
the images enhanced by our method have almost no noise, the most abundant and
reasonable color information and no over-exposure or under-exposure phenomena.
In addition, we test our model on LIME, DICM and MEF datasets. These three
frequently used datasets have no GroundTruth. As shown in Fig. LABEL:LIME,
Fig. LABEL:DICM and Fig LABEL:MEF, these methods have unpleasant problems,
such as color bias, noise and low brightness. In contrast, the output results
of our model can effectively suppress noise, reasonably improve brightness and
correctly restore color information. Our results can also achieve a very good
visual effect.

###  V-D Quantitative Performance Analysis

Higher values of PSNR, SSIM, FSIM and UQI and lower value of LPIPS indicate
better quality of images. We compared our method with other state-of-the-art
methods in terms of these metrics, including traditional methods such as MSRCR
[11], BIMEF [35], LIME [12], Dong [36], SRIE [34], MF [33], NPE [32], RRM
[19], LECARM [38], JED [20], PLM [61] and DIE [39] and deep learning methods
such as MBLLEN [18], RetinexNet [13], GLAD [14], RDGAN[16], Zero-DCE [17],
Zhang [48] and EnlightenGan [15]. As shown in Table I, our method achieves the
best performance in PSNR, SSIM, LPIPS, FSIM and UQI.
We also adopted Angular Error [59] as a quantitative index to measure the
degree of color bias. Lower values of Angular Error indicate less color bias
and better performance against color distortion. We compared four measures,
i.e., deltaE [60], the mean and median values of the Angular Error and the
average value of them on the LOL validation dataset, which contains 15 real-
world low/normal-light image pairs. As shown in Table II, compared with other
methods, our method achieves the best performance in all four measures.
In addition, we adopted Gray Entropy (GE) and Color Entropy (CE) as objective
indicators to measure the amount of information in the image. CE calculates
the information entropy of the three channels of the image and then adds them
up. Image information entropy is also a common index for image quality
evaluation. It reflects the richness of image information from the perspective
of information theory. In general, larger entropy values for an image
correspond to richer information and better quality. If the image brightness
is too low, all kinds of information in the image will be hidden by darkness,
so the calculated information entropy will be very low. We compare our method
with other state-of-the-art deep learning low-light image enhancement methods
in terms of GE and CE. As shown in Table III, our results have the highest
entropy of information, meaning our results contain the most information, such
as color, structure, texure and detail information.

The inference speed of our model is very fast. As shown in Fig. LABEL:speed,
we tested all of the deep learning models on a TITAN XP GPU with 12G RAM and
compared the inference speed and UQI index of our model with those of other
deep learning methods. The closer the method is to the upper left corner in
the figure, the faster the method is and the higher the UQI is. As shown in
the last colume of Table III, our model only takes 7 ms to process a real-
world low-light image with a resolution of 600x400 from the LOL dataset, and
introducing our NCBC Module does not slow down our framework. Zero-DCE [17] is
the fastest in terms of the inference speed, taking only 2 ms for a 600x400
resolution image. The speed of our model ranks second among all of the
compared methods. This is because Zero-DCE adopts a very lightweight DCE-Net
to estimate a set of best-fitting Light-Enhancement curves that iteratively
enhance a given input image, so it can be very fast in terms of inference
speed. Our model adopts two relatively deep U-Nets and a convolutional module,
but the decomposition task is simple, so the reasoning can be achieved very
quickly. In the brightness enhancement network, we only need to enhance the
brightness of the illumination map and generate a single-channel illumination
map correctly, and this is also a very simple task that can be accomplished
quickly. Denoising and color bias correction are the most time-consuming
steps. Our NCBC Module only calculates the loss functions, which enables our
model to achieve the functions of noise removal and color restoration without
additional time consumption on these two steps. This is why our model can run
at such a high speed.

###  V-E Failure Cases

There are still some shortcomings in our method. If an image contains some
areas that are too dark, unpleasant halo artifacts and local shadow will
appear in such areas of the processed image when using our model. For example,
as shown in Fig. LABEL:failure, our method may produce some failure cases with
shadows and white blocks.

## Conclusion

In this paper, inspired by the Retinex theory [21] and the idea of RetinexNet
[13], we propose a fast deep learning framework called BLNet, which consists
of two U-Nets and a plain CNN with several stacked convolutional blocks. In
the decomposition phase, we use the first U-Net to decouple images into
reflectance and illumination. We also propose a CNN-based Noise and Color Bias
Control Module (NCBC Module) to remove noise from the reflectance while
controlling color bias and preserving details by transferring some of the
details and color information to the illumination. In the enhancement phase,
we use a similar U-Net to enhance the brightness of the illumination.
Extensive experiments demonstrate the superiority and effectiveness of our
method. In particular, our method is able to fix all of the degradations, such
as low brightness, distorted contrast, real-world noise, color bias and detail
loss, that occur in the process of low-light image enhancement. Another
important advantage of our model is its fast computation speed, which is
achieved as a result of the time-saving noise removal and color restoration
contributed by our NCBC module.

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