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Invertible Network for Unpaired Low-light Image Enhancement [article]

Jize Zhang, Haolin Wang, Xiaohe Wu, Wangmeng Zuo
2021 arXiv   pre-print
Here, we propose to leverage the invertible network to enhance low-light image in forward process and degrade the normal-light one inversely with unpaired learning.  ...  Existing unpaired low-light image enhancement approaches prefer to employ the two-way GAN framework, in which two CNN generators are deployed for enhancement and degradation separately.  ...  CONCLUSION In this paper, we propose an invertible network based method for unpaired learning of the low-light image enhancement.  ... 
arXiv:2112.13107v1 fatcat:zx2ffwnzzjb6tgr2if5462vhem

Enhance Images as You Like with Unpaired Learning [article]

Xiaopeng Sun, Muxingzi Li, Tianyu He, Lubin Fan
2021 arXiv   pre-print
Low-light image enhancement exhibits an ill-posed nature, as a given image may have many enhanced versions, yet recent studies focus on building a deterministic mapping from input to an enhanced version  ...  In contrast, we propose a lightweight one-path conditional generative adversarial network (cGAN) to learn a one-to-many relation from low-light to normal-light image space, given only sets of low- and  ...  Acknowledgments We thank Jing Ren for useful suggestions on the manuscript and all the anonymous reviewers for their valuable comments.  ... 
arXiv:2110.01161v1 fatcat:nju7e7emnvhk5cdny5fjvnuofq

From Fidelity to Perceptual Quality: A Semi-Supervised Approach for Low-Light Image Enhancement

Wenhan Yang, Shiqi Wang, Yuming Fang, Yue Wang, Jiaying Liu
2020 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)  
To address these problems, we propose a novel semi-supervised learning approach for low-light image enhancement.  ...  A deep recursive band network (DRBN) is proposed to recover a linear band representation of an enhanced normal-light image with paired low/normal-light images, and then obtain an improved one by recomposing  ...  This method proves the feasibility to learn with unpaired data for low-light enhancement.  ... 
doi:10.1109/cvpr42600.2020.00313 dblp:conf/cvpr/Yang0FW020 fatcat:phezqwzofrflde2peuplsdgvdi

MARN: Multi-scale attention retinex network for low-light image enhancement

Xin Zhang, Xia Wang
2021 IEEE Access  
Many approaches have been proposed for low-light image enhancement.  ...  Unsupervised learning has also been used for low-light image enhancement. EnlightenGAN [26] proposes a highly effective unsupervised GAN for low-light enhancement.  ... 
doi:10.1109/access.2021.3068534 fatcat:k543pagyfrgelllbmvexizky5a

Semantically Contrastive Learning for Low-light Image Enhancement [article]

Dong Liang, Ling Li, Mingqiang Wei, Shuo Yang, Liyan Zhang, Wenhan Yang, Yun Du, Huiyu Zhou
2021 arXiv   pre-print
Low-light image enhancement (LLE) remains challenging due to the unfavorable prevailing low-contrast and weak-visibility problems of single RGB images.  ...  SCL-LLE allows the LLE model to learn from unpaired positives (normal-light)/negatives (over/underexposed), and enables it to interact with the scene semantics to regularize the image enhancement network  ...  , we design a novel semantically contrastive low-light image enhancement network.  ... 
arXiv:2112.06451v1 fatcat:2iri5zu6j5gltkdd4gufq7sphq

Unpaired Underwater Image Enhancement Based on CycleGAN

Rong Du, Weiwei Li, Shudong Chen, Congying Li, Yong Zhang
2021 Information  
To solve such issues, we propose a novel unpaired underwater image enhancement method via a cycle generative adversarial network (UW-CycleGAN) to recover the degraded underwater images.  ...  Finally, experimental results on two unpaired underwater image datasets produced satisfactory performance compared to the state-of-the-art image enhancement methods, which proves the effectiveness of the  ...  Enhancement for Improved Visual Perception (FUnIE-GAN-UP) [35] • Generative Adversarial Networks for Photo Cartoonization (CartoonGAN) [24] • Unpaired Image-to-Image Translation using Cycle Consistent  ... 
doi:10.3390/info13010001 fatcat:pe5cvpm3avg2rdtthnwhhjlk5a

D2BGAN: A Dark to Bright Image Conversion Model for Quality Enhancement and Analysis Tasks Without Paired Supervision

Jhilik Bhattacharya, Shatrughan Modi, Leonardo Gregorat, Giovanni Ramponi
2022 IEEE Access  
INDEX TERMS Image enhancement, generative adversarial network, unpaired supervision.  ...  This paper presents an image enhancement model, D2BGAN (Dark to Bright Generative Adversarial Network), to translate low light images to bright images without a paired supervision.  ...  The normalizing flows permit to model the conditional distribution of normally exposed images and then exploit the network invertibility to enhance low-light images.  ... 
doi:10.1109/access.2022.3178698 fatcat:qhfepj42c5d25dlucs5vvpzvge

Self-supervised Image Enhancement Network: Training with Low Light Images Only [article]

Yu Zhang, Xiaoguang Di, Bin Zhang, Chunhui Wang
2020 arXiv   pre-print
This paper proposes a self-supervised low light image enhancement method based on deep learning.  ...  With this model, a very simple network can separate the illumination and reflectance, and the network can be trained with low light images only.  ...  Dong et al. proposed an enhancement method that performs the dehazing operation after inverting the low light image and then inverts the image back [18] .  ... 
arXiv:2002.11300v1 fatcat:mnnkux5xtfeprn2atmvbynyx7a

A Switched View of Retinex: Deep Self-Regularized Low-Light Image Enhancement

Zhuqing Jiang, Haotian Li, Liangjie Liu, Aidong Men, Haiying Wang
2021 Neurocomputing  
A B S T R A C T Self-regularized low-light image enhancement does not require any normal-light image in training, thereby freeing from the chains of paired or unpaired training data that are time-consuming  ...  Our method is efficient as a low-light image is decoupled into two subspaces, i.e., color and brightness, for better preservation and enhancement.  ...  (Jiang et al., 2019) trained a generative adversarial network (GAN) with unpaired low-/normal-light images.  ... 
doi:10.1016/j.neucom.2021.05.025 fatcat:qfwoaf3r3fgrfg6gsjkqjeue2y

Low-Light Image Enhancement Based on Multi-Path Interaction

Bai Zhao, Xiaolin Gong, Jian Wang, Lingchao Zhao
2021 Sensors  
Experimental results show that the proposed method can effectively improve the visual quality of low-light images, and the performance is better than the state-of-the-art methods.  ...  In order to improve the quality of the image, in this paper, a multi-path interaction network is proposed to enhance the R, G, B channels, and then the three channels are combined into the color image  ...  [22] noticed that the inverted low-light images intuitively resembled images acquired in hazy lighting conditions; thus, low-lighting image enhancement has much in common with video haze removal.  ... 
doi:10.3390/s21154986 fatcat:l7xzhe6mljhuxaftzp7wd3ei7y

Unsupervised Low-Light Image Enhancement via Histogram Equalization Prior [article]

Feng Zhang, Yuanjie Shao, Yishi Sun, Kai Zhu, Changxin Gao, Nong Sang
2021 arXiv   pre-print
Deep learning-based methods for low-light image enhancement typically require enormous paired training data, which are impractical to capture in real-world scenarios.  ...  To address this issue, we propose an unsupervised low-light image enhancement method based on an effective prior termed histogram equalization prior (HEP).  ...  CONCLUSION In this work, we propose an unsupervised network for low-light image enhancement. Inspired by Retinex theory, we design a two-stage network to enhance the low-light image.  ... 
arXiv:2112.01766v1 fatcat:k3oslkc5k5birhlkpu4b6eamh4

Semi-supervised atmospheric component learning in low-light image problem [article]

Masud An Nur Islam Fahim and Nazmus Saqib and Jung Ho Yub
2022 arXiv   pre-print
If we know the desired ambient factors associated with the given low-light image, we can recover the enhanced image easily .  ...  Influenced by the above issues, this study presents a semisupervised training method using no-reference image quality metrics for low-light image restoration.  ...  To integrate [10] onto the low light enhancement problem, we first invert our low-light input image L(x) and the resultant image 1 − L(x), which is the 'hazy image' , for this problem.  ... 
arXiv:2204.07546v1 fatcat:4upueprfffdgbpic7gu3fzn7bm

CERL: A Unified Optimization Framework for Light Enhancement with Realistic Noise [article]

Zeyuan Chen, Yifan Jiang, Dong Liu, Zhangyang Wang
2022 arXiv   pre-print
We present Coordinated Enhancement for Real-world Low-light Noisy Images (CERL), that seamlessly integrates light enhancement and noise suppression parts into a unified and physics-grounded optimization  ...  Low-light images captured in the real world are inevitably corrupted by sensor noise.  ...  EnlightenGAN [9] is the first attempt that uses unpaired data to train a low-light enhancement model, of which the network architecture is based on the generative adversarial network. [11] Fig. 2  ... 
arXiv:2108.00478v2 fatcat:4kvlnqc4rfhytnwlwrzmaxmdiy

Progressive Joint Low-light Enhancement and Noise Removal for Raw Images [article]

Yucheng Lu, Seung-Won Jung
2021 arXiv   pre-print
Low-light imaging on mobile devices is typically challenging due to insufficient incident light coming through the relatively small aperture, resulting in a low signal-to-noise ratio.  ...  To tackle this problem, in this paper, we propose a low-light image processing framework that performs joint illumination adjustment, color enhancement, and denoising.  ...  Sean Moran for helping reproduce their results and strengthen this paper through constructive discussions.  ... 
arXiv:2106.14844v3 fatcat:ixovvbu5wjdzzij7ktq54ny6la

Multi-Frame GAN: Image Enhancement for Stereo Visual Odometry in Low Light [article]

Eunah Jung, Nan Yang, Daniel Cremers
2019 arXiv   pre-print
We propose the concept of a multi-frame GAN (MFGAN) and demonstrate its potential as an image sequence enhancement for stereo visual odometry in low light conditions.  ...  We base our method on an invertible adversarial network to transfer the beneficial features of brightly illuminated scenes to the sequence in poor illumination without costly paired datasets.  ...  In Table 3 , we compare MFGAN with other photo enhancing methods including adaptive histogram equalization(AHE) [17] , low-light image enhancement(LIME) [18] , deep photo enhancer(DP) [19] .  ... 
arXiv:1910.06632v1 fatcat:vwaz4kjfanhbrfm3y2jheeb4ji
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