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Low-Light Image Enhancement with Wavelet-Based Diffusion Models

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Shuaicheng LiuAuthors Info & Claims

ACM Transactions on Graphics (TOG), Volume 42, Issue 6

Article No.: 238, Pages 1 - 14

https://doi.org/10.1145/3618373

Published: 05 December 2023 Publication History

Abstract

Diffusion models have achieved promising results in image restoration tasks, yet suffer from time-consuming, excessive computational resource consumption, and unstable restoration. To address these issues, we propose a robust and efficient Diffusion-based Low-Light image enhancement approach, dubbed DiffLL. Specifically, we present a wavelet-based conditional diffusion model (WCDM) that leverages the generative power of diffusion models to produce results with satisfactory perceptual fidelity. Additionally, it also takes advantage of the strengths of wavelet transformation to greatly accelerate inference and reduce computational resource usage without sacrificing information. To avoid chaotic content and diversity, we perform both forward diffusion and denoising in the training phase of WCDM, enabling the model to achieve stable denoising and reduce randomness during inference. Moreover, we further design a high-frequency restoration module (HFRM) that utilizes the vertical and horizontal details of the image to complement the diagonal information for better fine-grained restoration. Extensive experiments on publicly available real-world benchmarks demonstrate that our method outperforms the existing state-of-the-art methods both quantitatively and visually, and it achieves remarkable improvements in efficiency compared to previous diffusion-based methods. In addition, we empirically show that the application for low-light face detection also reveals the latent practical values of our method. Code is available at https://github.com/JianghaiSCU/Diffusion-Low-Light.

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References

[1]

Yochai Blau, Roey Mechrez, Radu Timofte, Tomer Michaeli, and Lihi Zelnik-Manor. 2018. The 2018 PIRM challenge on perceptual image super-resolution. In European Conference on Computer Vision.

[2]

Alan C Bovik. 2010. Handbook of image and video processing. Academic press.

[3]

Stanley H Chan, Ramsin Khoshabeh, Kristofor B Gibson, Philip E Gill, and Truong Q Nguyen. 2011. An augmented Lagrangian method for total variation video restoration. IEEE Transactions on Image Processing 20, 11 (2011), 3097--3111.

Digital Library

[4]

François Chollet. 2017. Xception: Deep learning with depthwise separable convolutions. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 1251--1258.

[5]

Hyungjin Chung, Byeongsu Sim, and Jong Chul Ye. 2022. Come-closer-diffuse-faster: Accelerating conditional diffusion models for inverse problems through stochastic contraction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 12413--12422.

[6]

Kostadin Dabov, Alessandro Foi, Vladimir Katkovnik, and Karen Egiazarian. 2007. Image denoising by sparse 3-D transform-domain collaborative filtering. IEEE Transactions on Image Processing 16, 8 (2007), 2080--2095.

[7]

Chao Dong, Chen Change Loy, Kaiming He, and Xiaoou Tang. 2015. Image super-resolution using deep convolutional networks. IEEE transactions on pattern analysis and machine intelligence 38, 2 (2015), 295--307.

[8]

Ben Fei, Zhaoyang Lyu, Liang Pan, Junzhe Zhang, Weidong Yang, Tianyue Luo, Bo Zhang, and Bo Dai. 2023. Generative Diffusion Prior for Unified Image Restoration and Enhancement. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 9935--9946.

[9]

Xueyang Fu, Delu Zeng, Yue Huang, Xiao-Ping Zhang, and Xinghao Ding. 2016. A weighted variational model for simultaneous reflectance and illumination estimation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2782--2790.

[10]

Chunle Guo, Chongyi Li, Jichang Guo, Chen Change Loy, Junhui Hou, Sam Kwong, and Runmin Cong. 2020. Zero-reference deep curve estimation for low-light image enhancement. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 1780--1789.

[11]

Lanqing Guo, Chong Wang, Wenhan Yang, Siyu Huang, Yufei Wang, Hanspeter Pfister, and Bihan Wen. 2023. Shadowdiffusion: When degradation prior meets diffusion model for shadow removal. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 14049--14058.

[12]

Xiaojie Guo, Yu Li, and Haibin Ling. 2016. LIME: Low-light image enhancement via illumination map estimation. IEEE Transactions on Image Processing 26, 2 (2016), 982--993.

Digital Library

[13]

Alfred Haar. 1911. Zur theorie der orthogonalen funktionensysteme. Math. Ann. 71, 1 (1911), 38--53.

[14]

Jiang Hai, Yutong Hao, Fengzhu Zou, Fang Lin, and Songchen Han. 2021. A Visual Navigation System for UAV under Diverse Illumination Conditions. Applied Artificial Intelligence 35, 15 (2021), 1529--1549.

[15]

Jiang Hai, Zhu Xuan, Ren Yang, Yutong Hao, Fengzhu Zou, Fang Lin, and Songchen Han. 2023. R2rnet: Low-light image enhancement via real-low to real-normal network. Journal of Visual Communication and Image Representation 90 (2023), 103712.

Digital Library

[16]

Jiang Hai, Ren Yang, Yaqi Yu, and Songchen Han. 2022. Combining Spatial and Frequency Information for Image Deblurring. IEEE Signal Processing Letters 29 (2022), 1679--1683.

[17]

Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. 2017. Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in Neural Information Processing Systems 30 (2017).

[18]

Jonathan Ho, Ajay Jain, and Pieter Abbeel. 2020. Denoising diffusion probabilistic models. Advances in Neural Information Processing Systems 33 (2020), 6840--6851.

[19]

Ruibing Hou, Hong Chang, Bingpeng Ma, Shiguang Shan, and Xilin Chen. 2019. Cross attention network for few-shot classification. Advances in Neural Information Processing Systems 32 (2019).

[20]

Yifan Jiang, Xinyu Gong, Ding Liu, Yu Cheng, Chen Fang, Xiaohui Shen, Jianchao Yang, Pan Zhou, and Zhangyang Wang. 2021. Enlightengan: Deep light enhancement without paired supervision. IEEE Transactions on Image Processing 30 (2021), 2340--2349.

Digital Library

[21]

Daniel J Jobson, Zia-ur Rahman, and Glenn A Woodell. 1997. A multiscale retinex for bridging the gap between color images and the human observation of scenes. IEEE Transactions on Image processing 6, 7 (1997), 965--976.

Digital Library

[22]

Justin Johnson, Alexandre Alahi, and Li Fei-Fei. 2016. Perceptual losses for real-time style transfer and super-resolution. In European Conference on Computer Vision. 694--711.

[23]

Bahjat Kawar, Michael Elad, Stefano Ermon, and Jiaming Song. 2022. Denoising diffusion restoration models. Advances in Neural Information Processing Systems 35 (2022), 23593--23606.

[24]

Diederik P Kingma and Jimmy Ba. 2015. Adam: A Method for Stochastic Optimization. In International Conference on Learning Representations.

[25]

Edwin H Land. 1977. The retinex theory of color vision. Scientific American 237, 6 (1977), 108--129.

[26]

Chulwoo Lee, Chul Lee, and Chang-Su Kim. 2013. Contrast enhancement based on layered difference representation of 2D histograms. IEEE transactions on Image Processing 22, 12 (2013), 5372--5384.

Digital Library

[27]

Xiaozhou Lei, Zixiang Fei, Wenju Zhou, Huiyu Zhou, and Minrui Fei. 2022. Low-light Image Enhancement Using the Cell Vibration Model. IEEE Transactions on Multimedia (2022).

[28]

Chongyi Li, Chunle Guo, Linghao Han, Jun Jiang, Ming-Ming Cheng, Jinwei Gu, and Chen Change Loy. 2021a. Low-light image and video enhancement using deep learning: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 44, 12 (2021), 9396--9416.

[29]

Chongyi Li, Chun-Le Guo, Man Zhou, Zhexin Liang, Shangchen Zhou, Ruicheng Feng, and Chen Change Loy. 2023. Embedding Fourier for Ultra-High-Definition Low-Light Image Enhancement. In International Conference on Learning Representations.

[30]

Guofa Li, Yifan Yang, Xingda Qu, Dongpu Cao, and Keqiang Li. 2021c. A deep learning based image enhancement approach for autonomous driving at night. Knowledge-Based Systems 213 (2021), 106617.

Digital Library

[31]

Jichun Li, Weimin Tan, and Bo Yan. 2021b. Perceptual variousness motion deblurring with light global context refinement. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 4116--4125.

[32]

Jian Li, Yabiao Wang, Changan Wang, Ying Tai, Jianjun Qian, Jian Yang, Chengjie Wang, Jilin Li, and Feiyue Huang. 2019. DSFD: dual shot face detector. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 5060--5069.

[33]

Youwei Li, Haibin Huang, Lanpeng Jia, Haoqiang Fan, and Shuaicheng Liu. 2022. D2C-SR: A Divergence to Convergence Approach for Real-World Image Super-Resolution. In European Conference on Computer Vision. 379--394.

Digital Library

[34]

Jinxiu Liang, Jingwen Wang, Yuhui Quan, Tianyi Chen, Jiaying Liu, Haibin Ling, and Yong Xu. 2021. Recurrent exposure generation for low-light face detection. IEEE Transactions on Multimedia 24 (2021), 1609--1621.

Digital Library

[35]

Seokjae Lim and Wonjun Kim. 2020. DSLR: deep stacked Laplacian restorer for low-light image enhancement. IEEE Transactions on Multimedia 23 (2020), 4272--4284.

[36]

Haoning Lin and Zhenwei Shi. 2014. Multi-scale retinex improvement for nighttime image enhancement. optik 125, 24 (2014), 7143--7148.

[37]

Risheng Liu, Long Ma, Jiaao Zhang, Xin Fan, and Zhongxuan Luo. 2021. Retinex-inspired unrolling with cooperative prior architecture search for low-light image enhancement. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 10561--10570.

[38]

Yuen Peng Loh and Chee Seng Chan. 2019. Getting to know low-light images with the exclusively dark dataset. Computer Vision and Image Understanding 178 (2019), 30--42.

[39]

Kin Gwn Lore, Adedotun Akintayo, and Soumik Sarkar. 2017. LLNet: A deep autoen-coder approach to natural low-light image enhancement. Pattern Recognition 61 (2017), 650--662.

Digital Library

[40]

Andreas Lugmayr, Martin Danelljan, Andres Romero, Fisher Yu, Radu Timofte, and Luc Van Gool. 2022. Repaint: Inpainting using denoising diffusion probabilistic models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 11461--11471.

[41]

Ziwei Luo, Fredrik K Gustafsson, Zheng Zhao, Jens Sjölund, and Thomas B Schön. 2023a. Image Restoration with Mean-Reverting Stochastic Differential Equations. arXiv preprint arXiv:2301.11699 (2023).

[42]

Ziwei Luo, Fredrik K Gustafsson, Zheng Zhao, Jens Sjölund, and Thomas B Schön. 2023b. Refusion: Enabling large-size realistic image restoration with latent-space diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. 1680--1691.

[43]

Kede Ma, Kai Zeng, and Zhou Wang. 2015. Perceptual quality assessment for multi-exposure image fusion. IEEE Transactions on Image Processing 24, 11 (2015), 3345--3356.

Digital Library

[44]

Long Ma, Tengyu Ma, Risheng Liu, Xin Fan, and Zhongxuan Luo. 2022. Toward Fast, Flexible, and Robust Low-Light Image Enhancement. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 5637--5646.

[45]

Xiaojiao Mao, Chunhua Shen, and Yu-Bin Yang. 2016. Image restoration using very deep convolutional encoder-decoder networks with symmetric skip connections. Advances in Neural Information Processing Systems 29 (2016).

[46]

Anish Mittal, Anush Krishna Moorthy, and Alan Conrad Bovik. 2012a. No-reference image quality assessment in the spatial domain. IEEE Transactions on image processing 21, 12 (2012), 4695--4708.

Digital Library

[47]

Anish Mittal, Rajiv Soundararajan, and Alan C Bovik. 2012b. Making a "completely blind" image quality analyzer. IEEE Signal Processing Letters 20, 3 (2012), 209--212.

[48]

Chen Hee Ooi and Nor Ashidi Mat Isa. 2010. Quadrants dynamic histogram equalization for contrast enhancement. IEEE Transactions on Consumer Electronics 56, 4 (2010), 2552--2559.

Digital Library

[49]

Ozan Özdenizci and Robert Legenstein. 2023. Restoring vision in adverse weather conditions with patch-based denoising diffusion models. IEEE Transactions on Pattern Analysis and Machine Intelligence 45, 8 (2023), 10346--10357.

Digital Library

[50]

Jaemin Park, An Gia Vien, Jin-Hwan Kim, and Chul Lee. 2022. Histogram-Based Transformation Function Estimation for Low-Light Image Enhancement. In IEEE International Conference on Image Processing. IEEE, 1--5.

[51]

Jialun Peng, Dong Liu, Songcen Xu, and Houqiang Li. 2021. Generating diverse structure for image inpainting with hierarchical VQ-VAE. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 10775--10784.

[52]

Mengwei Ren, Mauricio Delbracio, Hossein Talebi, Guido Gerig, and Peyman Milanfar. 2022. Image Deblurring with Domain Generalizable Diffusion Models. arXiv preprint arXiv:2212.01789 (2022).

[53]

Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. 2022. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 10684--10695.

[54]

Olaf Ronneberger, Philipp Fischer, and Thomas Brox. 2015. U-net: Convolutional networks for biomedical image segmentation. In Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention. 234--241.

[55]

Chitwan Saharia, William Chan, Huiwen Chang, Chris Lee, Jonathan Ho, Tim Salimans, David Fleet, and Mohammad Norouzi. 2022. Palette: Image-to-image diffusion models. In ACM SIGGRAPH 2022 Conference Proceedings. 1--10.

Digital Library

[56]

Shuyao Shang, Zhengyang Shan, Guangxing Liu, and Jinglin Zhang. 2023. ResDiff: Combining CNN and Diffusion Model for Image Super-Resolution. arXiv preprint arXiv:2303.08714 (2023).

[57]

Kuldeep Singh, Rajiv Kapoor, and Sanjeev Kr Sinha. 2015. Enhancement of low exposure images via recursive histogram equalization algorithms. Optik 126, 20 (2015), 2619--2625.

[58]

Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli. 2015. Deep unsupervised learning using nonequilibrium thermodynamics. In International Conference on Machine Learning. 2256--2265.

[59]

Jiaming Song, Chenlin Meng, and Stefano Ermon. 2021a. Denoising Diffusion Implicit Models. In International Conference on Learning Representations.

[60]

Yang Song and Stefano Ermon. 2019. Generative modeling by estimating gradients of the data distribution. Advances in Neural Information Processing Systems 32 (2019).

[61]

Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole. 2021b. Score-Based Generative Modeling through Stochastic Differential Equations. In International Conference on Learning Representations.

[62]

Jian Sun, Wenfei Cao, Zongben Xu, and Jean Ponce. 2015. Learning a convolutional neural network for non-uniform motion blur removal. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 769--777.

[63]

Shuhang Wang, Jin Zheng, Hai-Miao Hu, and Bo Li. 2013. Naturalness preserved enhancement algorithm for non-uniform illumination images. IEEE Transactions on Image Processing 22, 9 (2013), 3538--3548.

Digital Library

[64]

Zhou Wang, A.C. Bovik, H.R. Sheikh, and E.P. Simoncelli. 2004. Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing 13, 4 (2004), 600--612.

Digital Library

[65]

Zhendong Wang, Xiaodong Cun, Jianmin Bao, Wengang Zhou, Jianzhuang Liu, and Houqiang Li. 2022. Uformer: A general u-shaped transformer for image restoration. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 17683--17693.

[66]

Zhixin Wang, Xiaoyun Zhang, Ziying Zhang, Huangjie Zheng, Mingyuan Zhou, Ya Zhang, and Yanfeng Wang. 2023. DR2: Diffusion-based Robust Degradation Remover for Blind Face Restoration. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 1704--1713.

[67]

Chen Wei, Wenjing Wang, Wenhan Yang, and Jiaying Liu. 2018. Deep Retinex Decomposition for Low-Light Enhancement. In British Machine Vision Conference.

[68]

Wenhui Wu, Jian Weng, Pingping Zhang, Xu Wang, Wenhan Yang, and Jianmin Jiang. 2022. Uretinex-net: Retinex-based deep unfolding network for low-light image enhancement. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 5901--5910.

[69]

Xiaogang Xu, Ruixing Wang, Chi-Wing Fu, and Jiaya Jia. 2022. SNR-aware low-light image enhancement. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 17714--17724.

[70]

Shaoliang Yang, Dongming Zhou, Jinde Cao, and Yanbu Guo. 2022. Rethinking low-light enhancement via transformer-GAN. IEEE Signal Processing Letters 29 (2022), 1082--1086.

[71]

Tao Yang, Peiran Ren, Xuansong Xie, and Lei Zhang. 2021a. Gan prior embedded network for blind face restoration in the wild. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 672--681.

[72]

Wenhan Yang, Shiqi Wang, Yuming Fang, Yue Wang, and Jiaying Liu. 2020a. From fidelity to perceptual quality: A semi-supervised approach for low-light image enhancement. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 3063--3072.

[73]

Wenhan Yang, Wenjing Wang, Haofeng Huang, Shiqi Wang, and Jiaying Liu. 2021b. Sparse gradient regularized deep retinex network for robust low-light image enhancement. IEEE Transactions on Image Processing 30 (2021), 2072--2086.

[74]

Wenhan Yang, Ye Yuan, Wenqi Ren, Jiaying Liu, Walter J Scheirer, Zhangyang Wang, Taiheng Zhang, Qiaoyong Zhong, Di Xie, Shiliang Pu, et al. 2020b. Advancing image understanding in poor visibility environments: A collective benchmark study. IEEE Transactions on Image Processing 29 (2020), 5737--5752.

[75]

Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Ming-Hsuan Yang. 2022. Restormer: Efficient transformer for high-resolution image restoration. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 5728--5739.

[76]

Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, Ming-Hsuan Yang, and Ling Shao. 2020. Learning enriched features for real image restoration and enhancement. In European Conference on Computer Vision. 492--511.

Digital Library

[77]

Rongkai Zhang, Lanqing Guo, Siyu Huang, and Bihan Wen. 2021. Rellie: Deep reinforcement learning for customized low-light image enhancement. In Proceedings of the 29th ACM International Conference on Multimedia. 2429--2437.

Digital Library

[78]

Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. 2018. The unreasonable effectiveness of deep features as a perceptual metric. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 586--595.

[79]

Dihan Zheng, Xiaowen Zhang, Kaisheng Ma, and Chenglong Bao. 2022. Learn from unpaired data for image restoration: A variational bayes approach. IEEE Transactions on Pattern Analysis and Machine Intelligence 45, 5 (2022), 5889--5903.

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Index Terms

Low-Light Image Enhancement with Wavelet-Based Diffusion Models
1. Computing methodologies
  1. Computer graphics
    1. Image manipulation
      1. Computational photography

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cover image ACM Transactions on Graphics

ACM Transactions on Graphics Volume 42, Issue 6

December 2023

1565 pages

ISSN:0730-0301

EISSN:1557-7368

DOI:10.1145/3632123

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Published: 05 December 2023

Published in TOG Volume 42, Issue 6

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Cited By

Yi AAnantrasirichai N(2024)A Comprehensive Study of Object Tracking in Low-Light EnvironmentsSensors10.3390/s2413435924:13(4359)Online publication date: 5-Jul-2024
https://doi.org/10.3390/s24134359
Pan JLiu XBai YZhai DJiang JZhao D(2024)Illumination-Aware Low-Light Image Enhancement with Transformer and Auto-Knee CurveACM Transactions on Multimedia Computing, Communications, and Applications10.1145/366465320:8(1-23)Online publication date: 29-Jun-2024
https://dl.acm.org/doi/10.1145/3664653
Liu JHu WYang ZChen JWang GChen XCai YGao HZhao H(2024)Rip-NeRF: Anti-aliasing Radiance Fields with Ripmap-Encoded Platonic SolidsACM SIGGRAPH 2024 Conference Papers10.1145/3641519.3657402(1-11)Online publication date: 13-Jul-2024
https://dl.acm.org/doi/10.1145/3641519.3657402
Cho HShin HJang YKo SJung S(2024)PD-CR: Patch-Based Diffusion Using Constrained Refinement for Image RestorationIEEE Signal Processing Letters10.1109/LSP.2024.338190831(949-953)Online publication date: 2024
https://doi.org/10.1109/LSP.2024.3381908
Wang WJin Z(2024)CAPformer: Compression-Aware Pre-trained Transformer for Low-Light Image Enhancement2024 IEEE International Conference on Multimedia and Expo (ICME)10.1109/ICME57554.2024.10688040(1-6)Online publication date: 15-Jul-2024
https://doi.org/10.1109/ICME57554.2024.10688040
Lian WLian WLuo Z(2024)Equipping Diffusion Models with Differentiable Spatial Entropy for Low-Light Image Enhancement2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)10.1109/CVPRW63382.2024.00661(6671-6681)Online publication date: 17-Jun-2024
https://doi.org/10.1109/CVPRW63382.2024.00661
Liu XWu ZLi AVasluianu FZhang YGu SZhang LZhu CTimofte RJin ZWu HWang CLing HCai YBian HZheng YLin JYuille AShao BGuo JLiu TWu MFeng YHou SLin HZhu YWu PDong WSun JZhang YYan QZou WYang WLi YWei QYe TChen SZhang ZZhao SWang BLuo YZuo ZWang MWang JWei YSun XGao YHuang JChen HChen XTang HChen YZhou YDai XQiu XDeng WGao QTong TLi MHu JHe XGuo XSabarinathan SUma KSasithradevi ABama BRoomi SSrivatsav VWang JSun LChen QShao JZhang YConde MFeijoo DBenito JGarcía ALee JKim SA SKhujaev NTsoy RMurtaza AKhairuddin UFaudzi AMalagi SJoshi AAkalwadi NDesai CTabib RMudenagudi ULian WLian WKalyanshetti JAralikatti VYashaswini PUpasi NHegde DPatil UC SYan XHao WFu MChoksy PSarvaiya AUpla KRaja KYan HZhang YLi BZhang JZheng H(2024)NTIRE 2024 Challenge on Low Light Image Enhancement: Methods and Results2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)10.1109/CVPRW63382.2024.00655(6571-6594)Online publication date: 17-Jun-2024
https://doi.org/10.1109/CVPRW63382.2024.00655
Dong WZhou HTian YSun JLiu XZhai GChen J(2024)ShadowRefiner: Towards Mask-free Shadow Removal via Fast Fourier Transformer2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)10.1109/CVPRW63382.2024.00625(6208-6217)Online publication date: 17-Jun-2024
https://doi.org/10.1109/CVPRW63382.2024.00625
Feng YHou SLin HZhu YWu PDong WSun JYan QZhang Y(2024)DiffLight: Integrating Content and Detail for Low-light Image Enhancement2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)10.1109/CVPRW63382.2024.00619(6143-6152)Online publication date: 17-Jun-2024
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Wang WYang HFu JLiu J(2024)Zero-Reference Low-Light Enhancement via Physical Quadruple Priors2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52733.2024.02462(26057-26066)Online publication date: 16-Jun-2024
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