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Invertible Network for Unpaired Low-light Image Enhancement
[article]
2021
arXiv
pre-print
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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]
2021
arXiv
pre-print
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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
2020
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
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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
2021
IEEE Access
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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]
2021
arXiv
pre-print
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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
2021
Information
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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
2022
IEEE Access
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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]
2020
arXiv
pre-print
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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
2021
Neurocomputing
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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
2021
Sensors
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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]
2021
arXiv
pre-print
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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]
2022
arXiv
pre-print
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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]
2022
arXiv
pre-print
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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]
2021
arXiv
pre-print
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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]
2019
arXiv
pre-print
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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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