11institutetext: Computer Science and Engineering, Indian Institute of
Tehnology, Bombay, India

# A Mixed-Supervision Multilevel GAN Framework for Image Quality Enhancement

Uddeshya Upadhyay  Suyash P. Awate We thank support from Aira Matrix and the
Infrastructure Facility for Advanced Research and Education in Diagnostics
grant funded by Department of Biotechnology, Government of India
(RD/0117-DBT0000-002).

###### Abstract

Deep neural networks for image quality enhancement typically need large
quantities of highly-curated training data comprising pairs of low-quality
images and their corresponding high-quality images. While high-quality image
acquisition is typically expensive and time-consuming, medium-quality images
are faster to acquire, at lower equipment costs, and available in larger
quantities. Thus, we propose a novel generative adversarial network (GAN) that
can leverage training data at multiple levels of quality (e.g., high and
medium quality) to improve performance while limiting costs of data curation.
We apply our mixed-supervision GAN to (i) super-resolve histopathology images
and (ii) enhance laparoscopy images by combining super-resolution and surgical
smoke removal. Results on large clinical and pre-clinical datasets show the
benefits of our mixed-supervision GAN over the state of the art.

###### Keywords:

Image quality enhancement, generative adversarial network (GAN), mixed-
supervision, super-resolution, surgical smoke removal.

##  1 Introduction and Related Work

Image quality enhancement using deep neural networks (DNNs) typically needs
large quantities of highly-curated training data comprising corresponding
pairs of low-quality and high-quality images. In this paper, “low-quality”
images refer to images that have low spatial resolution and exhibit other
degradations; “high-quality” images refer to high-resolution uncorrupted
images. While high-quality image acquisition is typically expensive and time-
consuming, medium-quality images are faster to acquire, at lower equipment
costs, and available in larger quantities. Thus, we propose a novel generative
adversarial network (GAN) that can leverage training data at multiple levels
of quality (e.g., high and medium quality) to improve performance while
limiting costs of data curation.

In pre-clinical and clinical digital histopathology, acquisition times
increase quadratically with decreasing pixel width [3, 8]. Furthermore,
higher-resolution digital scanners are more expensive. Super-resolution
algorithms can enable faster scanning at lower resolutions by filling-in the
fine detail in a post-processing step. Mixed-supervision can reduce the need
for data at the highest resolution and / or improve performance using medium-
resolution training data; our earlier works [16, 15] propose mixed-supervision
for image segmentation. A class of methods for super-resolution rely on
neighbor embeddings [5], sparse representation [13, 22], and random forests
[14]. DNNs have been successful at super-resolution with their ability to
optimize features and the regression mapping jointly [7, 4, 10]. The class of
DNNs giving among the best performances for super-resolution involve GANs
[11]. Our earlier work [18] on image super-resolution proposed loss functions
for robust learning in the presence of corrupted training data. Some methods
[19] use a sequence of GANs for image enhancement (without super-resolution),
but, unlike our approach, train each GAN independently, without defining a
unified loss function. However, none of these methods leverage training data
of multiple qualities for (learning) super-resolution.

In laparoscopy, higher-resolution imaging can offer the surgeon wider views
and fine details, both, in the same frame, without needing to move the
endoscope back (to get a larger field of view) and forth (to get the fine
details) [21]. While some state-of-the-art laparoscopic imaging offers 8K
ultra-high-definition (UHD) images, typical and affordable equipment offers
lower resolution (less than 2K) images. Laparoscopic images also suffer from
degradation because of surgical smoke. Super-resolution algorithms can enable
low-cost equipment to produce high-resolution images. Smoke-removal
algorithms, as well as super-resolution algorithms, can enhance the
performance of subsequent processing involving tracking, segmentation, depth
analysis, stereo vision, and augmented reality, in addition to improving the
surgeon’s visibility. Mixed-supervision learning can reduce the need for UHD
training sets. Most methods for image desmoking [2, 12, 17] model the
degradation analytically and propose algorithms to undo the degradation. On
the other hand, a very recent approach [6] relies on learning using a DNN. In
contrast, we propose a novel mixed-supervision GAN framework to leverage
training data of varying image qualities to improve performance in super-
resolution coupled with image restoration.

We propose a novel mixed-supervision GAN framework for image quality
enhancement that can leverage training data at multiple levels of quality,
e.g., high and medium quality, to improve performance while limiting costs of
data curation. To the best of our knowledge, our framework is the first to
propose image quality enhancement using data at multiple quality levels
simultaneously. We apply our framework for (i) super-resolution in
histopathology images and (ii) image quality enhancement in laparoscopy images
by combining super-resolution and surgical smoke removal. Results on large
clinical and pre-clinical datasets show the benefits of our mixed-supervision
GAN over the state of the art.

##  2 Methods

We describe our framework’s architecture, loss functions, and the training
scheme.

(a)

(b)

Figure 1:  Our Mixed-Supervision Multilevel GAN Framework for Quality
Enhancement: MLQEGAN. (a) Architecture for generator and discriminator, used
at each level, in (b) our multilevel architecture with mixed supervision.

Our Mixed-Supervision Multilevel GAN Architecture. While our framework is not
specific to a DNN architecture, we choose to use the generator and the
discriminator designs used in SRGAN [11] (Figure 1(a)). However, our
architecture (Figure 1(b)) incorporates a sequence of generators and
discriminators, with each level dealing with increasingly higher quality
training data, compared to the low-quality input data. We refer to a SRGAN-
like framework, equivalent to one level in our sequence, as quality-
enhancement GAN (QEGAN). We call our framework as multilevel QEGAN (MLQEGAN)
(Figure 1).

For QEGAN, let the generator $\mathcal{G}(\cdot;\theta)$, parametrized by set
$\theta$, take low-resolution and degraded (i.e., low-quality) input
$X^{\text{LQ}}$ and produce super-resolved and restored (i.e., high-quality)
output $X^{\text{HQ}}:=\mathcal{G}(X^{\text{LQ}};\theta)$ to match the
observed high-quality true image $X^{\text{T}}$. Following the GAN learning
principle, let the discriminator $\mathcal{D}(\cdot;\phi)$, parametrized by
set $\phi$, learn to discriminate between the probability density function
(PDF) of super-resolved and restored generator outputs $P(X^{\text{HQ}})$ and
the PDF of observed high-quality true images $P(X^{\text{T}})$.

For our MLQEGAN, let there be $L$ levels starting from level $1$ to level $L$.
Let level $l$ have generator $\mathcal{G}_{l}(\cdot;\theta_{l})$ and
discriminator $\mathcal{D}_{l}(\cdot;\phi_{l})$. Let the training data be in
the form of random-vector image pairs $(Y^{\text{LQ}},Y^{\text{T}}_{j})$,
where (i) $Y^{\text{LQ}}$ is the lowest-quality image and (ii)
$Y^{\text{T}}_{j}$ is the higher-quality image at quality level $j$ with
$j\in[2,L+1]$. In this paper, the levels in our MLQEGAN framework correspond
to different resolutions / pixel sizes. Typical applications lead to training
data in large quantities at medium quality as compared to data at higher
quality. Thus, our architecture has progressively fewer parameters to optimize
at higher levels, consistent with low sample sizes for higher quality training
data.

Training Set. For training MLQEGAN, a higher-quality training image
$Y^{\text{T}}_{j}$ at level $j$ can inform the training of generators and
discriminators at all levels $i<j$. Thus, from every pair of the form
$(Y^{\text{LQ}},Y^{\text{T}}_{j})$, we create multiple training instances
$(X^{\text{LQ}},X^{\text{T}}_{m})$ for all levels $m\leq j$, where (i)
$X^{\text{LQ}}:=Y^{\text{LQ}}$ and (ii) $X^{\text{T}}_{m}$ is the lower-
resolution (corresponding to the pixel size at level $m$) version of
$Y^{\text{T}}_{j}$; for $j=m$ we have $X^{\text{T}}_{m}=Y^{\text{T}}_{m}$.
Thus, for training MLQEGAN, we use an effective training set of the form
$\\{(X^{\text{LQ}},X^{\text{T}}_{m})\\}$ for all $m\in[2,L+1]$. Let the set of
parameters to be optimized be $\theta:=\\{\theta_{l}\\}_{l=1}^{L}$ and
$\phi:=\\{\phi_{l}\\}_{l=1}^{L}$. Let the union of the generator parameters at
levels from $1$ to $l$ be $\theta_{1}^{l}:=\\{\theta_{k}\\}_{k=1}^{l}$.

Loss Functions. We design a loss function $\mathcal{L}(\theta,\phi)$ as the
sum of loss functions corresponding to each level $l\in[1,L]$. The loss
function at level $l$ comprises a fidelity loss
$\mathcal{L}^{F}_{l}(\theta_{1}^{l})$ and an adversarial loss
$\mathcal{L}^{A}_{l}(\theta_{l},\phi_{l})$. The generator
$\mathcal{G}_{1}(\cdot;\theta_{1})$ at level $1$ takes the lowest-quality
input image $X^{\text{LQ}}$ and maps it to
$X^{\text{HQ}}_{2}:=\mathcal{G}_{1}(X^{\text{LQ}};\theta_{1})$. Let
$\mathcal{G}_{1}^{j}:=\mathcal{G}_{j}\circ\mathcal{G}_{j-1}\circ\cdots\circ\mathcal{G}_{1}$
be the composition of the sequence of generators from level $1$ to level
$j\in[2,L]$. The generator $\mathcal{G}_{l}(\cdot;\theta_{l})$ at level $l$
takes the lower-quality input image
$\mathcal{G}_{1}^{l-1}(X^{\text{LQ}};\theta_{1}^{l-1})$ and maps it to
$X^{\text{HQ}}_{l+1}:=\mathcal{G}_{l}(\mathcal{G}_{1}^{l-1}(X^{\text{LQ}};\theta_{1}^{l-1});\theta_{l})=\mathcal{G}_{1}^{l}(X^{\text{LQ}});\theta_{1}^{l})$.
We propose a fidelity loss between the generator output $X^{\text{HQ}}_{l+1}$
and the higher-quality image $X^{\text{T}}_{l+1}$ as

$\displaystyle\mathcal{L}^{F}_{l}(\theta_{1}^{l}):=E_{P(X^{\text{LQ}},X^{\text{T}}_{l+1})}[F(\mathcal{G}_{1}^{l}(X^{\text{LQ}};\theta_{1}^{l}),X^{\text{T}}_{l+1})],$

where $F(A,B)$ measures the dissimilarity between images $A$ and $B$; in this
paper, $F(A,B)$ is the mean squared error (MSE). The adversarial loss is the
Kullback-Leibler divergence between (i) the one-hot probability vectors
(distributions) for the generated (“fake”) image $X^{\text{HQ}}_{l+1}$ and
true (“real”) image $X^{\text{T}}_{l+1}$, i.e., $[0,1]$ or $[1,0]$, and (ii)
the probability vectors (distributions) for the generator-output image
$X^{\text{HQ}}_{l+1}$ and the higher-quality image $X^{\text{T}}_{l+1}$, i.e.,
$[\mathcal{D}_{l}(X^{\text{HQ}}_{l+1};\phi_{l}),1-\mathcal{D}_{l}(X^{\text{HQ}}_{l+1};\phi_{l})]$
or
$[\mathcal{D}_{l}(X^{\text{T}}_{l+1};\phi_{l}),1-\mathcal{D}_{l}(X^{\text{T}}_{l+1};\phi_{l})]$,
respectively. We propose the adversarial loss

$\displaystyle\mathcal{L}^{A}_{l}(\theta_{1}^{l},\phi_{l}):=E_{P(X^{\text{LQ}},X^{\text{T}}_{l+1})}[\log(1-\mathcal{D}_{l}(\mathcal{G}_{1}^{l}(X^{\text{LQ}};\theta_{1}^{l});\phi_{l}))+\log(\mathcal{D}_{l}(X^{\text{T}}_{l+1};\phi_{l}))].$

We propose the overall loss
$L(\theta,\phi):=\sum_{l=1}^{L}\lambda_{l}(\mathcal{L}^{F}_{l}(\theta_{l}^{l})+\alpha_{l}\mathcal{L}^{A}_{l}(\theta_{1}^{l},\phi_{l}))$,
where we fix $\lambda_{1}:=1$ and tune the free parameters
$\\{\lambda_{l}\\}_{l=2}^{L}\cup\\{\alpha_{l}\\}_{l=1}^{L}$ using cross
validation.

Training Scheme. We initialize the parameters $\theta\cup\phi$ sequentially,
as follows. We first initialize $\theta_{1}\cup\phi_{1}$ using the training
subset with image pairs of the form $(X^{\text{LQ}},X^{\text{T}}_{2})$ to
minimize the loss function
$\mathcal{L}^{F}_{1}(\theta_{1})+\alpha_{1}\mathcal{L}^{A}_{1}(\theta_{1},\phi_{1})$.
Then, for increasing levels $l$ from $2$ to $L$, we initialize
$\theta_{l}\cup\phi_{l}$ using the training subset
$(X^{\text{LQ}},X^{\text{T}}_{l})$ to minimize the loss function
$\mathcal{L}^{F}_{l}(\theta_{l}^{l})+\alpha_{l}\mathcal{L}^{A}_{l}(\theta_{1}^{l},\phi_{l})$,
but fixing all previous-level generator parameters $\theta_{1}^{l-1}$. After
initialization, we train all GANs in a joint optimization framework using Adam
[9], with internal parameters $\beta_{1}:=0.9,\beta_{2}:=0.999$, initial
learning rate $0.002$ that decays based on cosine annealing, batch size $8$,
and number of epochs $500$.

##  3 Results and Discussion

We evaluate our mixed-supervision MLQEGAN to (i) super-resolve histopathology
images and (ii) enhance laparoscopy images by combining super-resolution and
surgical smoke removal. We compare against a SRGAN-like architecture that is
the among the state of the art for super-resolution, which we leverage
additionally for quality enhancement, i.e., QEGAN. To quantitate performance,
we use three complementary measures: (i) relative root MSE (RRMSE) between the
DNN output, say, $A^{\text{HQ}}$ and the corresponding true image, say,
$A^{\text{T}}$, as $\|A^{\text{HQ}}-A^{\text{T}}\|_{2}/\|A^{\text{T}}\|_{2}$,
(ii) multiscale structural similarity index (msSSIM) [20], and (iii) quality
index based on local variance (QILV) [1]. While msSSIM is more sensitive to
image noise than blur, QILV is more sensitive to image blur instead. In this
paper, the cross-validation tuned free-parameter values are: $\forall
l,\alpha_{l}=3e^{-5}$; when $L$$=$$2$, $\lambda_{1}=1e^{-4}$, $\lambda_{2}=1$;
when $L$$=$$3$, $\lambda_{1}=1e^{-4}$, $\lambda_{2}=1^{e-4}$, $\lambda_{3}=1$.

(a1) Low Res. $64^{2}$ (b1) QEGAN (c1) Our MLQEGAN (d1) Truth $256^{2}$

(a2) Low Res. $64^{2}$ (b2) QEGAN (c2) Our MLQEGAN (d2) Truth $256^{2}$

Figure 2:  Results on Histopathology Images. (a1)-(a2) Low-resolution 642
(from 10$\times$ magnification). (RRMSE, msSSIM, QILV) for: (b1)-(b2) QEGAN
4$\times$ super-resolution (2562): (0.131, 0.868, 0.830), (0.117, 0.837,
0.919). (c1)-(c2) Our MLQEGAN 4$\times$ super-resolution (2562): (0.117,
0.910, 0.90), (0.091, 0.922, 0.942). (d1)-(d2) Truth 2562 (from 40$\times$
magnification).

(a1) Histopath. 4$\times$SR (a2) Histopath. 4$\times$SR (a3) Histopath.
4$\times$SR

(b1) Lap. 4$\times$SR+Desmoke (b2) Lap. 4$\times$SR+Desmoke (b3) Lap.
4$\times$SR+Desmoke

(c1) Lap. 8$\times$SR+Desmoke (c2) Lap. 8$\times$SR+Desmoke (c3) Lap.
8$\times$SR+Desmoke

Figure 3:  Results on Histopathology and Laparoscopy Images: Quantitative
Analysis. RRMSE, msSSIM, QILV for images in: (a1)-(a3) histopathology:
4$\times$ super-resolution; (b1)-(b3) laparoscopy: 4$\times$ super-resolution
+ desmoking; (c1)-(c3) laparoscopy: 8$\times$ super-resolution + desmoking.
The error bars indicate the variability from randomness in data sampling and
Adam [9].

Results on Histopathology Images. We take $8$ whole-slide histopathology
images from pre-clinical Wistar rat biopsies at 40$\times$ magnification from
a Hamamatsu scanner, which includes images at multiple magnification levels,
including 10$\times$ and 20$\times$. The task is to map the low-quality input
data $X^{\text{LQ}}$ at 10$\times$ to the high-quality data $X^{\text{T}}$ at
40$\times$. MLQEGAN uses $L$$=$$2$ levels.

The training set comprises image-pair instances of
$(Y^{\text{LQ}},Y^{\text{T}}_{2})$ and $(Y^{\text{LQ}},Y^{\text{T}}_{3})$. We
create instances of $(Y^{\text{LQ}},Y^{\text{T}}_{2})$ by (i) randomly
selecting $5000$ non-overlapping patches at 20$\times$ (1282 pixels) to give
$Y^{\text{T}}_{2}$; (ii) selecting the corresponding patches at 10$\times$
(642 pixels) to give $Y^{\text{LQ}}$. We create instances of
$(Y^{\text{LQ}},Y^{\text{T}}_{3})$ by (i) randomly selecting non-overlapping
patches at 40$\times$ (2562 pixels) to give $Y^{\text{T}}_{3}$; (ii) selecting
the corresponding patches at 10$\times$ (642 pixels) to give $Y^{\text{LQ}}$.
We vary the number of the highest-quality image-pair instances of
$(Y^{\text{LQ}},Y^{\text{T}}_{3})$ from $50$ to $750$.

We create the validation set of image-pair instances of
$(V^{\text{LQ}},V^{\text{T}}_{3})$ by randomly choosing $100$ images at
40$\times$ to give $V^{\text{T}}_{3}$, and their corresponding patches at
10$\times$ to give $V^{\text{LQ}}$. We similarly create the test set of $1000$
instances of $(Z^{\text{LQ}},Z^{\text{T}}_{3})$.

While QEGAN can leverage only a small subset of available training data
comprising instances of the form $(Y^{\text{LQ}},Y^{\text{T}}_{3})$, our
MLQEGAN is able leverage the entire training set including instances of the
forms $(Y^{\text{LQ}},Y^{\text{T}}_{3})$ and
$(Y^{\text{LQ}},Y^{\text{T}}_{2})$. Our MLQEGAN outputs (Figure 2(c1)-(c2))
are much closer to the ground truth (Figure 2(d1)-(d2)), compared to QEGAN
outputs (Figure 2(b1)-(b2)). Unlike QEGAN, MLQEGAN is able to extract useful
information in the medium-quality images $Y^{\text{T}}_{2}$ available in
significantly larger quantities, compared to the highest-quality images
$Y^{\text{T}}_{3}$. In this way, MLQEGAN clearly outperforms QEGAN in
reproducing the true textural appearances (colors and features) and cell
shapes. Quantitatively (Figure 3(a1)–(a3)), while QEGAN’s performance reduces
steadily with the reduction in the number of training-set images at the
highest-quality level, our MLQEGAN’s performance remains much more stable.
Moreover, our MLQEGAN’s performance consistently stays significantly better
than QEGAN’s performance, for all training-set sizes.

(a1)Low Qual. 32$\times$64 (b1)QEGAN (c1)Our MLQEGAN (d1)Truth 128$\times$256

(a2)Low Qual. 32$\times$64 (b2)QEGAN (c2)Our MLQEGAN (d2)Real 128$\times$256

(a3)Low Qual. 16$\times$32 (b3)QEGAN (c3)Our MLQEGAN (d3)Truth 128$\times$256

(a4)Low Qual. 16$\times$32 (b4)QEGAN (c4)Our MLQEGAN (d4)Real 128$\times$256

Figure 4:  Results on Laparoscopic Images: Super-Resolution and Desmoking.
(RRMSE, msSSIM, QILV) are in parentheses. (a1) Input with 4$\times$ lower-
resolution, simulated smoke; (b1) QEGAN: (0.112,0.897,0.963); (c1) Our
MLQEGAN: (0.065,0.971,0.993); (d1) Truth. (a2) Input with 4$\times$ lower-
resolution, real smoke; (b2) QEGAN; (c2) Our MLQEGAN; (d2) Real smoky high-
resolution image. (a3) Input with 8$\times$ lower-resolution, simulated smoke;
(b3) QEGAN: (0.151,0.825,0.926); (c3) Our MLQEGAN: (0.098,0.925,0.988); (d3)
Truth. (a4) Input with 8$\times$ lower-resolution, real smoke; (b4) QEGAN;
(c4) Our MLQEGAN; (d4) Real smoky high-resolution image.

Results on Laparoscopic Images. We use the da Vinci dataset [23]. Our task
involves smoke removal and 4$\times$ super-resolution. MLQEGAN uses $L$$=$$2$.

The training set comprises image-pair instances of
$(Y^{\text{LQ}},Y^{\text{T}}_{2})$ and $(Y^{\text{LQ}},Y^{\text{T}}_{3})$. We
create instances of $(Y^{\text{LQ}},Y^{\text{T}}_{2})$ by (i) randomly
selecting $5000$ smokeless full-resolution images and 2$\times$ downsampling
them to give $Y^{\text{T}}_{2})$; (ii) degrading the same selected image set
with smoke, and then 4$\times$ downsampling (we smooth a bit using Gaussian
convolution before downsampling to prevent aliasing) to create the low-quality
training set $Y^{\text{LQ}}$. We create instances of
$(Y^{\text{LQ}},Y^{\text{T}}_{3})$ by (i) randomly selecting full-resolution
smokeless images to give $Y^{\text{T}}_{3}$; (ii) degrading and downsampling
the same selected image set to give $Y^{\text{LQ}}$. We vary the number of the
highest-quality image pairs $(Y^{\text{LQ}},Y^{\text{T}}_{3})$ from $50$ to
$500$.

We create the validation set of image-pair instances of
$(V^{\text{LQ}},V^{\text{T}}_{3})$ by randomly choosing $100$ images
$V^{\text{T}}_{3}$ and creating $V^{\text{LQ}}$ by degrading and downsampling
as before. We similarly create the test set of $1000$ instances of
$(Z^{\text{LQ}},Z^{\text{T}}_{3})$.

While QEGAN learning is unable to use medium-quality images, our MLQEGAN uses
them to improve performance significantly. With simulated smoke, results from
our MLQEGAN (Figure 4(c1)) appear sharper than those from QEGAN (Figure
4(b1)). We also evaluate the trained models for QEGAN and MLQEGAN on real-
world smoke by selecting smoky frames (Figure 4(d2)) and downsampling them by
4$\times$ to create the low-quality input (Figure 4(a2)). The results from
MLQEGAN (Figure 4(c2)) show sharper and more realistic textures. Analogous to
the aforementioned experiments, we tested all methods in a very challenging
scenario with 8$\times$ downsampling, where MLQEGAN uses $L$$=$$3$ levels.
Here as well, MLQEGAN performs better than QEGAN (Figure 4(a3)-(d4)).

Quantitatively, with simulated smoke, for 4$\times$ and 8$\times$ downsampling
(Figure 3(b1)-(c3)) shows that MLQEGAN outperforms QEGAN in all measures.

Time Requirements. Our MLQEGAN has fewer layers and fewer parameters (by
roughly 20%) than SRGAN (or QEGAN). Training time can depend on many factors,
e.g., network architecture, training set sizes, and epochs needed to converge.
Our MLQEGAN uses more training images at lower quality, but with smaller
sizes. In general, our training time is a bit larger than that of QEGAN. For
test data, our MLQEGAN produces the output a bit faster than SRGAN (or QEGAN)
because we have fewer parameters and layers.

Conclusion. We proposed a novel mixed-supervision GAN that leverages training
data at multiple levels of quality to improve performance while limiting costs
of data curation. We propose a novel multilevel architecture with a sequence
of GANs with (i) progressively decreasing complexity and (ii) loss functions
using coupled generator sequences. We apply our mixed-supervision GAN to (i)
super-resolve histopathology images and (ii) enhance laparoscopy images by
combining super-resolution and surgical smoke removal. Results on large
datasets show the benefits of our mixed-supervision GAN over the state of the
art.

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