L2BGAN: An image enhancement model for image quality improvement and image analysis tasks without paired supervision
Authors: Jhilik Bhattacharya
Gianni Ramponi
Leonardo Gregorat
Shatrughan Modi
Venue: ICLR 2022 Submitted (2022)

Abstract: The paper presents an image enhancement model,
L2BGAN, to translate low light images to bright images
without a paired supervision. We introduce the use of geo-
metric and lighting consistency along with a contextual loss
criterion. These when combined with multiscale color, tex-
ture and edge discriminators prove to provide competitive
results. We perform extensive experiments on benchmark
datasets to compare our results visually as well as objec-
tively. We observe the performance of L2BGAN on real time
driving datasets which are subject to motion blur, noise and
other artifacts. We further demonstrate the application of
image understanding tasks on our enhanced images using
DarkFace and ExDark datasets.


Under review as a conference paper at ICLR 2022
L2BGAN:
AN IMAGE ENHANCEMENT MODEL FOR IM- AGE QUALITY IMPROVEMENT AND IMAGE ANALYSIS TASKS WITHOUT PAIRED SUPERVISION
Anonymous authors Paper under double-bling review
ABSTRACT
The paper presents image enhancement model, L2BGAN, to translate low light images to bright images without paired supervision. We introduce the use of geometric and lighting consistency along with a contextual loss criterion. These when combined with multiscale color, texture and edge discriminators prove to provide competitive results. We perform extensive experiments on benchmark datasets to compare our results visually as well as objectively. We observe the performance of L.2BGAN on real time driving datasets which are subject to motion blur, noise and other artifacts. We further demonstrate the application of image understanding tasks on our enhanced images using DarkFace and ExDkrk datasets.
INTRODUCTION
Image enhancement prerequisite for many computer vision based image understanding tasks. In particular, it is very crucial to enhance low light or dark images to obtain images which not only have better image aesthetic quality but can also be suitably processed for object detection and face detection tasks. The task by itself is one of the earliest studied domains in computer vision, but the continuous evolution of computational resources and deep learning architectures has generated paradigm shift towards the latter approach. A thorough literature review shows three main ways in which the problem approached: histogram equalization (Ibrahim & Pik Kong 2007 Nakai al.) 2013), Retinex-based Guo al. (2017): Wang al. (2016); Fu et al. (2016). and machine learning based eL. 2018 Wang et al. 2019 Jiang et 2021 Guo et al. 2020). The primary challenge many noise sources in the acquisition of poorly lit scenes. These include readout, photon shot, dark current, and fixed pattern noises, in addition to photon response non-uniformities. The noise level increases while treating lightness and contrast of low light images, more so in case of compressed-ddnnmmics images. Ap- plying denoising filter prior light enhancement will result in blurring, while the reverse causes noise amplification as seen in Fig. [ Hence, dealing with denoising and low-light enhancement problems simultaneously using a learning based approach seems be the optimal choice.
while dealing with low light image enhancement tasks is that there
The success of deep learning enhancement models depend on the availability of large scale anno-
Figure 1: Original Dark Image, Processed Image with LIME (noise amplified), Processed Image with MBLLEN (artifacts due to smoothing), from left to right
tated data. For the present problem, there is a requirement of paired images such that the low-light image serves as input while its brighter counterpart serves the target image. The Adobe 5k dataset [Vladimir et al. 201T) serves as a benchmark used by many researchers for this task. This dataset provides original low light image, retouched by five different photographic experts, one of which

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is used as the target of supervised training. Some researchers have also used synthetic paired set for the same by intentionally transforming a bright image to generate its dark counterpart. This transformation being global, its suitability for challenging real time images is arguable. Ideally, training procedure without the requirement of paired supervision is the most appropriate solution. Very few works have been reported on low light image enhancement using unpaired images (Tiang] et al. 2021 Ni et al., 2020). Moreover, even though image to image style translation without paired supervision is a popular approach [Anoosheh et al. (2019): Fu et al. (011);; Zhu et al. (2017b), not many have considered the mapping of low light images to bright light images as style transfer problem. In this work, we consider the low light image enhancement task as an image style trans- fer problem. We train deep neural architecture using unsupervised image pairs. This promotes the use of available real time dark and bright images without the need of pairwise annotations or synthetic treatment. We resort to GANS for training using two encoders and two decoders. We use three discriminators to learn color, texture, and edges separately. The unsupervised image to image translation problem is challenging as it aims to recover a joint probability given marginal probability. For this purpose, one of the most common approaches is to add a cycle consistency constraint along with the adversarial losses. This bijective constraint becomes restrictive in cases as the task at hand, where the low light image domain may contain less information compared to its bright counterpart. To handle this situation we use a geometric consistency constraint which ensures that an image from one domain and its geometric transformed version generate the same image in the other domain. Additionally, a contextual loss constraint ensures that the context and semantics of the images are preserved. This paper has the following main contributions: (i) it poses the low to bright light enhancement problem as an unpaired image translation problem; (ii) uses a geometric and an illumination consistency constraint in the images; (iii) it uses a contextual loss to measure semantic similarity; (iv) it uses multiscale color, texture and edge discriminators per domain; (v) it demonstrates the effectiveness of the approach for image understanding tasks. While none of such items are individually novel, exploiting their ensemble effect in this task is indeed a significant novel contribution. Our paper is organized in the following manner. We provide related background of the task in Sec. 2 The proposed technique is discussed in details in Sec. Experimental results and conclusion is provided in Secs. and respectively.
RELATED BACKGROUND
Histogram based enhancement aims to improve the image quality by modifying the image his- togram. This can result in overstreiched contrast which lacks in naturalness. Using this framework different techniques have been developed. BPDHE (Ibrahim & Pik Kong 2007) uses an HE on a dynamic range expanded version of sub-histograms obtained by the local maxima of the input image histogram. FHSABP (Wang et al., 20088) solves a convex optimization problem to obtain the flattest target histogram with the brightness preservation constraint. Due to their brightness preservation, (lbrahim & Pik Kong 2007) and (Wang et al.] 2008) are not suitable to tackle the low light image enhancement, but prevent overstretching. Morover, (Ibrahim & Pik Kong 2007) and (Wang et al. 2008) do not consider relationships between adjacent pixels, whereas methods such as HMF (Arici et al. 2009) CEBGA (Hashemi et al.) 2009) and DHECI (Nakai et al. 2013) do. In HMF (Arici et al. 2009) the target histogram is obtained via a parametric optimization problem, involving the local variance of pixels. In CEBGA CEBGA(Hashemi Hashemii al. 2009) a genetic algorithm is adopted to enhance the contrast of the images modifying the histogram. using as a fitness function the number of edges
in the enhanced image. DHECI (Nakai et al. 2013) performs a histogram equalization on the differ- ential intensity histogram and the differential saturation histogram from the HSI color space. More advanced techniques which embed contextual information are CVC (Celik & Tjahjadi 2011 and LDR (Lee et al. 2013). which use 2D histograms.
Retinex based methods use the assumption that an image can be pixel-wise decomposed into re- flectance and illumination. In (Li et al.] (011) classic approach is proposed, where the lightness is first decomposed in reflex lightness and ambient illumination. Reflectance is then extracted from reflex lightness, while the ambient illumination is log-transformed and used for the output image. A widely used framework for the Retinex based enhancement methods is the fusion based framework, as in FEMWII Fu et al. (2016) and FMSBIE Wang et al. (2016). Both estimate the illumination ing the pixel-wire hise maximum in the RGB color space, which is used to obtain the reflectance. Three different modified illuminations are then obtained through different techniques to improve bright-
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ness and contrast. These illumination maps are then fused with a multiscale approach. Multiscale techniques are also used to extract reflectance (Liu et al. 2016)
Another approach for the illumination estimation is proposed in LIME (Guo et al. 2017), where an optimization problem is used to obtain a smooth, duructure-preservve
illumination map, which is then enhanced through a gamma correction. As the noise is critical in poorly illuminated images in (Guo et ] 2017) a denoising techinque is also proposed: the output reflectance is the linear combination of the original one and a BM3D-filtered one, using the illumination as the weight to prevent the oversmoothing of bright areas. In (Li et al., 201T), Fu et al. (0166). |Wang et al.) (2016), (Liu et al. (016)) and (Guo et al. 2017) reflectance is obtained by the estimated illmination. More advanced techniques. such as SRIE Fu et al.] (2016) and JIEPMR Cai et al. (2017), jointly decompose reflectance and illumination. While the Retinex theory has proved to be suited for low light image enhancement, this approach suffers from color distortion and hand-crafted illumination manipulation. Learning based methods can overcome these problems, while also providing better denoising, which is crucial in low light images.
Data learning methods include a variety of methods from deep learning, such as autoencoders, Deep CNNs and GANs, all of which have proved to be suitable techniques to enhance image quality and brightness.
In LLNet (Lore et al. 2017). GLADNet (Wang et al. 2018) and ALED-Net (Li et al. 2020), de- noising autoencoders are used. Despite the good results, the simplicity of these methods limits their capability compared more complex learning methods. In (Ren et al.) 2019) multiple autoencoders are used to work separately on the content and on the edges of the image while also using CNN, RNN and more advanced loss function, including discriminator and VGG-16 net. An autoen- coder is also used for DeepUPE (Wang et al.] 2019) to learn a image-to-illuminatoo mapping used for fashion- enhancing, while considering reconstruction loss, color loss and smoothness of the illuminance. In (Jiang & Zheng 2019), an end-to-end U-Net is used, with a 3D convolution in order deal with videos, preventing flickering in consecutive frames, and using the GRBG com- ponents from the camera sensor as an input. In MBILEN (Lv et al.) 2018). CNN is applied to the input image. Then, the output of each layer is fed to independent autoencoders. The obtained output image is weighted sum with learnable weights of the autoencoders output. Considering strutural, contextual and regional loss for the training. While autoencoders and U-Net are conve- nient for image-to- -mage nets, methods such as Tao et al. (2017) and (Ignatov al. 2017) use only deep neural networks. In particular, these use residual convolutional neural networks to mitigate the vanishing gradient problem in deep networks. In (Ignatov et al. 2017), the CNN enhances images from low-end devices to professional DSLR quality, using a dataset with multiple acquisition of the same scene from different devices for the training, which is difficult to obtain. TToo al. (2017) uses special-designed residual CNN modules to create a deep CNN, but is then trained using only the SSIM. Apart from (Wang et al.) 2019), also RetinexNet (Wei et al. 2018) and Kind++ (Zhang et al.
2021) augment the Retinex theory, outperforming classic Retinex based methods. Both (Wei
lt al. 2018 and (Zhang et al.] (021) use CNN decompose the reflectance and illumination components. In both cases, denoising take advantage of the illumination map, as in (Guo et al.) 2017), to improve the result over plain denoising. In (Wei et al. 2018). the BM3D is used for de- noising whereas in (Zhang et al. (021) a specific CNN with multi-scal illumination consideration is proposed. For illumination adjustment [Wei et al. 2018) uses an encoder decoder structure while (Zhang et al. 202T) adopt CNN with a free parameter to modify the adjustment ratio. A com- mon critical aspect about the methods considered above is data availability. Since these methods supervised, they require paired low/normal light images of the same scene. A widely used way to obtain them is to artificially darken normal light images, but this impacts the naturalness of the results. Recently, methods such as (Wei et al. 2018) and (Zhang al.. 2021) used datasets with images of the same scenes with different exposures. This proved to be effective, but such datasets are scarce and tedious to create. To overcome the data availability issue, several unsupervised and unpaired supervised methods have been developed. ZDCE (Guo et al.) 2020) is an unsupervised low-light image enhancer which uses a CNN to obtain pixel-wise enhancement curves to adjust the input image, using non-reference loss functions for the training. EGAN (Jiang et al. (02T) and UE- GAN (Ni et al. (020) use a GAN to obtain a light enhancement without paired supervision, using a decoder-encoder for the generation. In (Jiang et al.) 2021) local and global discriminator are used while in (Ni et al. 2020) only one multi-scale discriminator is used. The promising results of JTiang et al. 2021) and
NNi et al. 2020), together with the availability of unpaired low/normal light
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images, makes GANs an appealing approach for low light image enhancement techniques.
Our contribution is similar to EGAN and UEGAN as it uses a GAN architecture with unpaired super- vision. Different from these, we do not a global attention. We rather use separate discriminators for color, texture and edge. Further we make use of cycle consistency, geometric and illumination consistency with contextual loss instead of perceptual losses.
3 METHODOLOGY
CycleGAN(Zhu et al. 2017a) was one of the first technique used for unsupervised image transla- tion, where a cycle consistency loss was used to learn the semantic dissimilarity between the two transfer domains. This replaced the direct paired loss. This translation is open ended problem, as a single image in a domain may result in multiple images in the other domain. While there are techniques which deal with the multimodal nature of the problem (Zhu et al., (017b) by random sampling or using more than one target, we focus on generating a single translation at a time. Other than the cycle consistency, many regularizations are used on the generators to enable the creation of real-generated images while using unpaired learning. These include penalizing the distance in the latent space, perceptual loss, and foreing the generators be close the identity function. In this work we relax the number of regularizations on the generator while ensuring that the generator is able to learn inverse mappings. We aim to map images in low light domain, LzN bright images By. Both L_ and By consist of a finite number of samples. An image Xreat belonging to Tag can be mapped to fake in domain Bg using an encoder F.z and a decoder Dy. Yfake - (Far (Xreat))) Similarly, X fake -> D, (YYea))) The cycle consistency can be computed using Y, fake to regen- crate X Xyecon using D,, Ey (Y, (Yfake)). Additionally, to set up the geometric and lighting consistency constraint, we transform X into X-g and X_I, where X-9 is a 90 degrees rotation of X and X_ gamma transformation of X. When Yakk is generated, it should be similar to y-kyake and Y -9 The inverse mapping refers to the inverse rotation of the reconstructed image. This ensures that the generator does not add further artifacts in the image while transforming it. Two discriminators De and De are used for the two domains. Further, each discriminator is divided into three parts: Deze. Dext and Dcre. These aim at discriminating color (æc). texture (æt) and edge (ze) differ- ences between the real and the generated images in Lg domain. The inclusion of these discriminators facilitates learning color distributions, texture distributions and edge distributions from unpaired im- ages. The color discriminator uses blurred RGB images. More precisely, since we have observed increased performances using multiscale discriminators, we use four discriminators: Dexe1, Deze2, Dezt and Deze instead of three. Here Deze1 and Deac2 denote RGB images blurred with two dif- ferent factors. A grayscale version of the image is used as the texture image, while the edge image is obtained via a Prewitt operator. The overall objective function is shown in Eq. 4 It includes the general adversarial loss Igann along with the cycle reconstruction loss Loye and the consistency loss Legeroon. While the adversarial loss and the cycle reconstruction loss are computed over both domains, the consistency losses are computed only on the target domain. Further, instead of using an L1 loss contextual loss is used in case of geometric and lighting consistency measurements. There are significant differences in the network performance with variations in the loss functions. We split the training process into four different stages: (i) first, we train the network with adver- sarial loss T.yan (X, Y) and cycle consistency loss Leege X X reom,, Y, Yrocon) using color, texture and edge discriminators. We call this version L2B _bsee (ii) we next use geometric consistency (eyooYyake Y -akke fakes X -9aake) only color images in the discriminator and use L1 loss criterion for consistencies. We denote this as GC; (iii) in the third phase we replace Li loss criterion in (ii) with contextual loss. We name the result GC.con; (iv) to obtain the final network version we add Leye.con loss to base. Unlike GC we only compute the consistencies in the target domain. Also, the L1 loss criterion for consistency measurements is replaced by contextual loss. We call the final result I. 1,2GAN. The same is depicted in f
I along with adversarial loss and cycle-consistency. We use
Lgan (X,Y) = (D(()) - 1)91 [, (E,,)))))))
(1)
Leye(X (X, Xrecor Y, Y,Yrecon) = E, 1/|1|1 |||ooeeoonn - X|11] + |& Yrecon Y|11
(2)
Lecy..om (X, X Xrecon; Y, Y.Yrecon) = E,g Xrecoon x1(c] + Ebll V /recon - [c]
(3)
Tnot Togan X) Tegan (Y,X) + Teyel (X, X Xreeon; Y, YrYrooo))
+ Iroge.oon (Yrakes Y gyaker Yfaees Y lfake)
(4)
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Pooe
Egan
Diye
Dis
Ey
Dy
Dige
Drye
Diys
Locuth
Figure 2: Block Diagram of L.2GAAN.
4 EXPERIMENTAL RESULTS
We used mixture of images from different datasets for training network. We included images from the Adobe Dataset and the Brightening Train dataset. 3000 images were randomly selected for training. The network was trained for total of 150 epochs with a learning rate of 0.0002. The images were randomly cropped at size 256 x 256. Training was done on workstation with an RTX6000 24GB GPU. Each training took approximately 36 hours. Initially we show some ablation study in section 4.1 For testing we use benchmark datasets DICM, LIME, MEF, NPE and backlit images. We compare the proposed work with state-of-the-art techniques like EGAN, MBLLEN, ZDCE, DeepUPE, LIME, FMSBIE. For all these methods softwares provided by the authors were utilised. Additionally we present image understanding results on the Berkeley Driving datasets and challenging low light datasets like DarkFace and ExDark. In Sec. 4.2 we present visual and objective evaluation of our technique. We use no-reference image quality assessment tools such NIQE, PIQE and BRISQUE for objective evaluations, since ground truth for these data is not available. In Sec. 4.3 we evaluate the LOL dataset using PSNR and SSIM scores. In Sec. 4.4 we present visual and objective evaluation on the real time Berkeley Driving dataset. Finally in Sec. 4.5 we provide some evaluations of our technique on the DarkFace and Exdark datasets.
4.1 ABLATION STUDY
In Fig. we demonstrate the performance of each network discussed in We observe that the processed images have visible blocking artifacts and color saturation for (ii) and (iii). Hence, use of (a) color, texture and edge discriminator, (b) geometric and lighting consistency on target domain, (c) contextual loss in place of L1 loss prove to be significant parts of the L2BGAN. These results are further backed by processing the Exdark dataset with each of the networks and computing the IQA scores for them. We observe that the best PIQE, NIQE and BRISQUE scores are obtained for L2BGAN (32.24; 2.96; 22.7). The GC' version provides the second best scores (32.84; 3.18; 22.97). Both L2B base and GC _Conooncccoc show comparatively poor performance in case of visual results as well as objective scores. For example, 1.2Bbase scores are (36.8477; 3.5547; 25.1551),
4.2 EVALUATION ON BENCHMARK LOW LIGHT DATASET-A
In Table I we demonstrate the image quality scores obtained after preprocessing the original dark images each case with the respective techniques. The scores shown are the average score of the entire dataset. It is observed that our proposed technique L2BGAN obtains competitive results. NIQE tool provides the best results for DCIM, LIME and MEF datasets when L2BGAN is applied. For NPE, L2BGAN provides the second best score. The LIME and NPE datasets also show high- est performance with L2BGAN when PIQE image quality tool is used. It is however noted that L2BGAN does not perform well on backlit images. Also for BRISQUE tool, DeepUPE provides the maximum scores whereas L2BGAN obtains the second best scores. We also show some visual comparisons in Fig. 4 More comparisons be found in 9 We observe that for EGAN the front
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Figure 3: The images in each row from left right are resultant images from GC, GC_con, L2B_ba __bae and the complete L2BGAN, respectively
Table 1: Objective no-reference image quality scores on different benchmark datasets. (Values in red and blue are best and second best, respectively)
DATASET
PIQE
NIQE
BRISQUE
PIQE
NIQE EGAN
BRISQUE
PIQE
NIQE
BRISQUE
DeepUPE
LIME
DICM
29.7
3.14
20.84
28.04
2.73
21.88
34.27
2.91
24.72
LIME
35.19
3.76
26.23
34.75
3.52
20.87
40.76
4.44
22.09
MEF
35.3
3.16
17.86
32.22
2.89
23.26
38.76
3.67
23,36
NPE
33.2
3.29
21.5
33.96
3.34
27.34
38,87
3.9
27.1
backlit
26.42
2.97
19.24
22.8
25.44
27.64
2.83
34.93
MBLLEN
ZDCE
FMSBIE
DICM
40.18
2.91
27.87
25.94
2.69
24.87
29.19
2.66
21.63
LIME
52.3
3.89
30.6
37.26
23.75
38.63
4.21
27.99
MEF
54.59
3.52
32.22
34.63
3.33
25.27
34.95
3.39
23.54
NPE
45.02
3.46
31.34
37.62
3.94
29.1
40.98
4.01
28.72
backlit
42.9
3.21
37,88
21.17
2.61
39.64
29.71
2.99
28.47
L2BGAN
DICM
28.27
2.5
20.89
LIME
33.25
3.26
21.66
MEF
32.96
2.78
20.94
NPE
29.42
3.31
27.21
backlit
30.31
3.09
28.29
building is relatively bright: the same happens for ZDCE the tower area. histogram analysis shows that for EGAN, ZDCE, DeepUPE and FMSBIE the red and/or green channel saturate. EGAN and LIME yield noisy halos near the bright portion of the tower; the dark sky areas away from the objects noise is visible. MBLLEN is unable to provide sharp details in the front building. The example image shown in has noise artifacts. Indeed, we have selected this image to demonstrate that our technique provides the least amount of artifacts when compared to other contemporary ones.
4.3 EVALUATION ON BENCHMARK LOW LIGHT DATASET-B
In Table we demonstrate the image quality scores obtained on LOL dataset after preprocessing the original dark images in each case with the respective techniques. As groundtruth data is available for LOL., we also show PSNR and SSIM scores for the same in Table It is observed that our proposed technique L2BGAN obtains the best scores for both cases and for all the image quality assessment tools. Sample Low light image, its ground truth, and the processed version using L2BGAN are shown in 5ig 5 low. However, the original PSNR and SSIM scores are 8.18 and 0.17 respectively (averaged on 789 test images). Although the scores of the processed images still low, they largely improved over the original ones as well as the ones obtained using other techniques. It should be noted that these real-world (not synthetic) images; i.e. they represent actual problem may face.
It is observed that PSNR (16.53) and SSIM (0.57) for the processed images are quite

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Figure 4: Sample original DICM image (top left) and its processed versions using Lime, FMSBIE, DeepUPE (first row, from second column to fourth column), and using MBLLEN, ZDCE, EGAN and L2BGAN (second row, from left to right).
Table 2:
Objective no-reference image quality scores along with SSIM and PSNR on the LOL
dataset
DeepUPE
EGAN
FMSBIE
LIME
MBLLEN
ZDCE
L2BGAN
PIQE
34.16
41.77
50.85
55,83
42.33
47.27
26.13
NIQE
10.63
7.67
12.04
11.81
5,41
11.21
3.15
BRISQUE
33.09
30.04
38.34
39.23
28.48
36.86
22.13
PSNR
10.70
15.31
12.16
14.45
16.59
13.42
16.53
SSIM
0.3233
0.4918
0.3995
0.3901
0.5134
0.4252
0.5742
Figure 5: Original low light, GT, processed images with MBLLEN and L2BGAN from left to right
Table 3: Objective no-reference image quality scores on BDD.
DPe
EGAN
FMSBIE
LIME
MBLLEN
ZDCE
L22GAN
PIQE
67.70
68.75
72.50
73.65
64.84
72.01
61.17
NIQE
3.62
3.21
4.25
4.13
3.58
4.29
3.12
BRISQUE
46.56
36.53
46.89
47.85
46.91
49.02
36.03

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Figure 6: Sample original BDD image (top left) and its processed versions using Lime, FMSBIE, DeepUPE (first row, from second column to fourth column), and using MBLLEN, ZDCE, EGAN and 1.2BGAN (second row, from left to right).
4.4 EVALUATION ON REAL TIME DRIVING DATASET
In Table we demonstrate the image quality scores obtained on the Berkeley Driving Dataset (BDD) after preprocessing the original dark images in each case with the respective techniques. The Berke- ley Driving Dataset consists of road scenes captured during day and night. We select dark images by filtering images with a global mean smaller than 30. The scores shown the average score of the entire dataset. It is observed that our proposed technique L,2GAAN obtains the best scores for all the image quality assessment tools. Images shown in Fig. depict that blocking artifacts and light reflections are enhanced for EGAN and LIME providing poor images. These problems are not visible for FMSBIE, MBLLEN and L2BGAN. Also, the enhancement provided by L2BGAN is smoother and brighter in the road regions without color saturation and changes in the sky region.
4.5 EVALUATION OF IMAGE UNDERSTANDING TASKS
One of the most important purposes of low light enhancements is to perform image understanding on it. As shown in case of Berkeley Driving dataset, a brighter and noise free image of the road will enable more accurate object detection tasks. Exdark and DarkFace are two benchmark datasets for object and face detection respectively. The DarkFace dataset has 6000 training and validation images and is particularly challenging since it presents an extremely dark environment with strong noise. Enhancing the images amplifies this noise making the face detection task very difficult. We randomly select two testsets of 500 images each from the 6000 image dataset and use the remaining 5000 as training. We obtain a mAP of 0.209 and 0.218 on the original darkface images for the two testsets when a pretrained Retinaface (trained on Widerface dataset) detector is applied. After applying preprocessing with L2BGAN the mAp is 0.301 and 0.331 respectively. We also test the mAP after applying EGAN and obtain 0.285 and 0.331 respectively. Finetuning the pretrained net- work with preprocessed DarkFace images gives a mAP of 0.525 and 0.407 on L2BGAN and EGAN respectively. Fig. 7 shows some example original and preprocessed images (using L2BGAN) along with the detections obtained. More examples can be found in figures 11 and 12 in the Appendix. is seen that the number of detected faces increases after preprocessing. We use S3FD face de- tectors and YOLOV3 object detectors show visual results. Training and finetuning is obtained via Tinaface detector. Such experiments primarily aim at showing how pre-processing the images using our L2BGAN model improves the results of any s-o-a detection algorithm. All experiments hence report detection results using a standard algorithm post enhancement application. For exam- ple, ssoos two false detections on the original image. On the processed image one more face is detected, with no false detections. Although it would be interesting to train the detection network jointly along with the enhancement network, currently not covered in our scope.
We also perform 2 simple experiments to analyze the quality of the classification features and object detection scores obtained from the L2BGAN images compared to the low light images. We take low and GGT) images from the LOL dataset and get L2BGAN images by processing the low images. We further compute distance d2 and d1 for each layer of VGG-19 trained with Imagenet. d2 and dl are obtained using feature difference of normal-L2BGAN images and normal-low image pairs respectively. We then compute a distance index (d1 - d2)/(d1 + d2) as shown in figure lleft). We observe that (a) d1 is greater than d2, hence L.2GAAN features are closer normal for all
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Figure 7: Face detection performances on original (first column) and preprocessed image using L,2GAN (second column) from DARKFACE (first row) and ExDark (second row) dataset.
100
Distance
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3.5
Disunee lodox
0.6
13
0.4
225
0.2
come:
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comp-5
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Yee
Figure 8: The figure on the left depicts the distance index obtained from classification features of low.normal and processed image. The right figure depicts the object detection scores using low and processed images.
layers,(b) although the distance index reduces as we move towards higher layers, still greater than 0, (c) another interesting observation is that both low and I.2BGAN features closest the normal between conv9-10 10 after which they increase again. Hence extracting features from this layer may prove beneficial. In (right) we compare the object detection scores of low and L2BGAN images. 40 images are picked from the LOL dataset for this purpose. It is observed that L2BGAN in general provides greater mean score and lower deviation.
5 CONCLUSION
We present an unpaired GAN based image enhancement operation using cycle consistency, geomet- ric and illumination consistency. Visual and objective results presented on benchmark datasets show that L2BGAN provides competitive results. It is observed that L2BGAN is able to enhance real images suffering from typical artifacts, without considerably amplifying the blocking artifacts. The images show smooth enhancements without color saturation in most cases. We have particularly fo- cused on Jpeg images even though a simple experiment on RAW data is discussed in the Appendix. Applications of L2BGAN for processing images be used for face detection show significant im- provement. would be interesting to see how joint training and domain adaptation can influence L22GAN to provide superior results on datasets like FarkFace.

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Under review as a conference paper at ICLR 2022
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APPENDIX
Figure 9: Sample original MEF image (top left) and its processed versions using LumeFFMMBIE,,eePUUPP row, from second column fourth column), and using MBLLEN, ZDCE, EGAN and L2BGAN (second row, from left to right).
5.1 EVALUATION ON RAW IMAGES
There is some research where image enhancement task is carried on data as input, which possess a lot of information not present in JPEG or PNG versions. Our aim is algorithm for enhancing images whose raw version is available: this indeed the case for many applications where post post-processing needs to be done on displayable formats. We have however performed a simple experiment where we have processed raw image using our network, without any retraining. The results were better than the ones obtained using MBLLEN and can be found in fig 10
Figure 10: Original low light raw, GT, processed with L2BGAN and MBLLEN from left to right
Figure 11: Face detection performances on original and preprocesse images from DARKFACE dataset. First column original while the second column is the processed version using L2BGAN.
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person SON
por
bench
Figure 12: Face and object detection performances on original and preprocessed image from ExDark dataset. First column original while the second column is the processed version using L2BGAN.
13


Reviews:
[{"review_id": "McIJigBFPK6", "reviewer": "Reviewer_1V61", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "Strengths:\nThis paper works on a CV task that is low-light image enhancement without paired data. It brings geometric and lighting prior to serving as assistant to the supervision. \n\nWeaknesses:\n\nThis work focus on low light image enhancement. The title is improper and too broad.\n\nThe organization and writing of the paper are poor. For example, the introduction section has two paragraphs. The method only has one paragraph which mixes everything together.\n\nThe novelty of the paper is weak. Geometric and lighting priors are two frequently used priors for unsupervised low-light image enhancement. The constraints are based on simple priors. Specifically, it uses the image after rotation as the geometric prior and uses grammar corrected image as the lighting prior. \n\nSome terms are used before explained, which makes them hard to understand. \n\nThe organization of Table 1 is also confusing.\n\nIn the experiments, more state-of-the-art methods should be compared.\n\nAn ablation study is necessary to show the effectiveness of the proposed priors and loss.", "summary_of_the_paper": "This paper proposes a low light image enhancement method. It proposes using geometric and lighting consistency and a contextual loss. ", "summary_of_the_review": "This paper works on unsupervised low-light image enhancement. The novelty is weak. The organization and writing are poor. The validation needs improvement.", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "1: The contributions are neither significant nor novel.", "empirical_novelty_and_significance": "1: The contributions are neither significant nor novel."}, "extra_text": {}}, {"review_id": "L_5gixXJy1g", "reviewer": "Reviewer_Et8P", "summary": "", "questions": "", "limitations": "", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "The main strength of the paper is represented by the results that it is able to obtain on standard datasets and how it compares to the state of the art.\nOn the downside, the main weakness of the paper is that none of the individual contributions is individually novel, e.g.:\n- casting the low light enhancement problem as an unpaired image translation problem\n- using geometric and illumination consistency\n- using a contextual loss for semantic similarity\n- using multiscale color, texture and edge discriminators\nThe paper can be therefore seen mainly as an incremental step in the field, and therefore not significant enough to permit its acceptance in this venue.\n\n", "summary_of_the_paper": "The authors propose a new image enhancement specific for low light images. They exploit the concepts of geometric and lighting consistency together with a contextual loss criterion. \nExtensive experiments are performed on benchmark datasets to compare their results both visually and objectively. ", "summary_of_the_review": "The work is incremental and uses already existing building blocks", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}, {"review_id": "GGPgYaf9GjT", "reviewer": "Reviewer_efio", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "1. The novelty in this paper is unclear. The paper mentions that \"While none of such items are individually novel, exploiting their ensemble effect in this task is indeed a significant novel contribution.\" I am not sure if assembling the existing architecture and losses can be sufficient to warrant the publication of this paper in ICLR. Moreover, the results do not show any significant improvement over those of the existing methods.\n\n2. It is well known in the community that unpaired GAN (or cycleGAN) can produce hallucination (fake contents). This important problem, particularly in image restoration/enhancement is not mentioned or addressed in this paper.\n\n3. The paper mentions dealing with denoising and low-light enhancement problems simultaneously as the motivation. But why the method can deal with noise is unclear. More specifically, how the network balances the noise suppression and the over-smoothing effect is unclear.\n\n4. The paper employs the grayscale image as the texture image. Why a simple grayscale operation can keep the texture information is unclear.  What is the benefit of using grayscale compared to rgb in low-light texture preservation?  \n\n5. These papers also use unpaired images:\n* Yifan Jiang, Xinyu Gong, Ding Liu, Yu Cheng, Chen Fang, Xiaohui Shen, Jianchao Yang, Pan Zhou, and Zhangyang Wang. Enlightengan: Deep light enhancement without paired supervision. IEEE  Transactions on Image Processing, 30:2340–2349, 2021. \n*Asha Anoosheh, Torsten Sattler, Radu Timofte, Marc Pollefeys, and Luc Van Gool. Night-to-day mage translation for retrieval-based localization. In 2019 International Conference on Robotics and Automation (ICRA), pp. 5958–5964. IEEE, 2019. \n* Sharma et al., \"Nighttime Stereo Depth Estimation using Joint Translation-Stereo Learning: Light Effects and Uninformative Regions.\" 2020 International Conference on 3D Vision (3DV). IEEE, 2020. \n* Sharma et al., \"Nighttime Visibility Enhancement by Increasing the Dynamic Range and Suppression of Light Effects\", Computer Vision and Pattern Recognition, CVPR 2021.", "summary_of_the_paper": "This paper proposes an image enhancement method using an unpaired-GAN based translation network (cycleGAN). It combines geometric, lighting consistency, and a contextual loss criterion. The geometric consistency is basically 90 rotations of the input image, while lighting consistency is basically gamma transformation.", "summary_of_the_review": "The three important aspects of a good paper, i.e., contributions, novelty and performance, are missing in the paper.", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "2: Several of the paper’s claims are incorrect or not well-supported.", "technical_novelty_and_significance": "1: The contributions are neither significant nor novel.", "empirical_novelty_and_significance": "1: The contributions are neither significant nor novel."}, "extra_text": {}}, {"review_id": "8l2x8jrG05I", "reviewer": "Reviewer_Fu5c", "summary": "", "questions": "", "limitations": "", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "* Strengths:\n\nTheir model shows competitive performance for the benchmarks on DICM, LIME, MEF, NPE datasets. Their model outperforms previous models for the benchmarks on the LOL and BDD datasets.\n\nPreprocessing the dart images by applying their model leads to better face detection performance compared to EnligthenGAN (Jiang et al., 2021). It also improves object detection performance somewhat, but it was not compared with previous works. \n\n* Weaknesses:\n\nDescriptions on their methodology in Section 3 is quite sketchy and confusing to follow. It is hard to get a clear picture of their formation on the full losses. \n\nIt is unclear what exactly their contextual loss is like. Eq. (3) is totally misleading if they adopted a contextual loss from \"The contextual loss for image transformation with non-aligned data, R. Mechrez et al. ECCV 2018\", which is never cited nor discussed clearly in the paper. \n\nAccording to EnligthenGAN (Jiang et al., 2021) and UEGAN (Ni et al., 2020), the use of cycle-consistency is discouraged as it make the network more complex and harder to train. They need to address this issue and justify the use of cycle-consistency denied by recent similar works.\n\nThe ablation study is poorly made. They fail to convince the effectiveness of color, texture and edge discriminators, as well as geometric and lighting consistency. \n\nReferences were not made properly: no citation on the contextual loss, no citation on the datasets used for their training and evaluation.", "summary_of_the_paper": "This paper proposes an image enhancement model to translate a low light image to a bright image without a paired supervision through CycleGAN. Compared to similar recent GAN-based works such as EnligthenGAN (Jiang et al., 2021) and UEGAN (Ni et al., 2020), the proposed work relies on cycle-consistency, as well as geometric and illumination consistency. They also separate their discriminators for color, texture and edges.", "summary_of_the_review": "The proposed model seems to lead some performance improvement in some benchmarks. However, the proposed methodology is not entirely convincing, requiring deeper analysis and justifications compared to recent GAN-based models. Also, it is hard to get a clear idea on their methodology in detail, as the paper is poorly written. ", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}]

Author rebuttal:

Meta-review: None of the reviewers championed the paper. Many weaknesses were shared across the reviewers: none of the individual contributions is individually novel, paper is not well written and the results do not show significant improvement over the prior state of the art. No rebuttal was provided. The AC agrees with the reviewers that the paper is not ready for publication at ILCR.
Decision: Reject