Fetching the paper…
Reading the bibliography…
Diffusion models have achieved promising results in image restoration tasks, yet suffer from time-consuming, excessive computational resource consumption, and unstable restoration.
Zur theorie der orthogonalen funktionensysteme
Alfred Haar. 1911 · 1911
Earlier work this paper cites.
The retinex theory of color vision
Edwin H Land. 1977 · 1977
Earlier work this paper cites.
A multiscale retinex for bridging the gap between color images and the human observation of scenes
Daniel J Jobson, Zia-ur Rahman, and Glenn A Woodell. 1997 · 1997
Earlier work this paper cites.
Image quality assessment: from error visibility to structural similarity
Zhou Wang, A.C. Bovik, H.R. Sheikh, and E.P. Simoncelli. 2004 · 2004
Earlier work this paper cites.
Handbook of image and video processing
Alan C Bovik. 2010 · 2010
Earlier work this paper cites.
Quadrants dynamic histogram equalization for contrast enhancement
Chen Hee Ooi and Nor Ashidi Mat Isa. 2010 · 2010
Earlier work this paper cites.
An augmented Lagrangian method for total variation video restoration
Stanley H Chan, Ramsin Khoshabeh, Kristofor B Gibson, Philip E Gill, and Truong Q Nguyen. 2011 · 2011
Earlier work this paper cites.
No-reference image quality assessment in the spatial domain
Anish Mittal, Anush Krishna Moorthy, and Alan Conrad Bovik. 2012a · 2012
Earlier work this paper cites.
Making a “completely blind” image quality analyzer
Anish Mittal, Rajiv Soundararajan, and Alan C Bovik. 2012b · 2012
Earlier work this paper cites.
Contrast enhancement based on layered difference representation of 2D histograms
Chulwoo Lee, Chul Lee, and Chang-Su Kim. 2013 · 2013
Earlier work this paper cites.
Naturalness preserved enhancement algorithm for non-uniform illumination images
Shuhang Wang, Jin Zheng, Hai-Miao Hu, and Bo Li. 2013 · 2013
Earlier work this paper cites.
Multi-scale retinex improvement for nighttime image enhancement
Haoning Lin and Zhenwei Shi. 2014 · 2014
Earlier work this paper cites.
Image super-resolution using deep convolutional networks
Chao Dong, Chen Change Loy, Kaiming He, and Xiaoou Tang. 2015 · 2015
Earlier work this paper cites.
Adam: A Method for Stochastic Optimization. In International Conference on Learning Representations
Diederik P Kingma and Jimmy Ba. 2015 · 2015
Earlier work this paper cites.
Perceptual quality assessment for multi-exposure image fusion
Kede Ma, Kai Zeng, and Zhou Wang. 2015 · 2015
Earlier work this paper cites.
U-net: Convolutional networks for biomedical image segmentation. In Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention . 234–241
Olaf Ronneberger, Philipp Fischer, and Thomas Brox. 2015 · 2015
Earlier work this paper cites.
Enhancement of low exposure images via recursive histogram equalization algorithms
Kuldeep Singh, Rajiv Kapoor, and Sanjeev Kr Sinha. 2015 · 2015
Earlier work this paper cites.
Deep unsupervised learning using nonequilibrium thermodynamics. In International Conference on Machine Learning . 2256–2265
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli. 2015 · 2015
Earlier work this paper cites.
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
Jian Sun, Wenfei Cao, Zongben Xu, and Jean Ponce. 2015 · 2015
Earlier work this paper cites.
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
Xueyang Fu, Delu Zeng, Yue Huang, Xiao-Ping Zhang, and Xinghao Ding. 2016 · 2016
Earlier work this paper cites.
LIME: Low-light image enhancement via illumination map estimation
Xiaojie Guo, Yu Li, and Haibin Ling. 2016 · 2016
Earlier work this paper cites.
Perceptual losses for real-time style transfer and super-resolution. In European Conference on Computer Vision . 694–711
Justin Johnson, Alexandre Alahi, and Li Fei-Fei. 2016 · 2016
Earlier work this paper cites.
Image restoration using very deep convolutional encoder-decoder networks with symmetric skip connections
Xiaojiao Mao, Chunhua Shen, and Yu-Bin Yang. 2016 · 2016
Earlier work this paper cites.
Xception: Deep learning with depthwise separable convolutions. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 1251–1258
François Chollet. 2017 · 2017
Earlier work this paper cites.
Gans trained by a two time-scale update rule converge to a local nash equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. 2017 · 2017
Earlier work this paper cites.
LLNet: A deep autoencoder approach to natural low-light image enhancement
Kin Gwn Lore, Adedotun Akintayo, and Soumik Sarkar. 2017 · 2017
Cited alongside, same era.
The 2018 PIRM challenge on perceptual image super-resolution. In European Conference on Computer Vision
Yochai Blau, Roey Mechrez, Radu Timofte, Tomer Michaeli, and Lihi Zelnik-Manor. 2018 · 2018
Cited alongside, same era.
Deep Retinex Decomposition for Low-Light Enhancement. In British Machine Vision Conference
Chen Wei, Wenjing Wang, Wenhan Yang, and Jiaying Liu. 2018 · 2018
Cited alongside, same era.
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
Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. 2018 · 2018
Cited alongside, same era.
Cross attention network for few-shot classification
Ruibing Hou, Hong Chang, Bingpeng Ma, Shiguang Shan, and Xilin Chen. 2019 · 2019
Cited alongside, same era.
Low-light Image Enhancement Using the Cell Vibration Model
Xiaozhou Lei, Zixiang Fei, Wenju Zhou, Huiyu Zhou, and Minrui Fei. 2022 · 2022
Later among the works it cites.
D2C-SR: A Divergence to Convergence Approach for Real-World Image Super-Resolution. In European Conference on Computer Vision . 379–394
Youwei Li, Haibin Huang, Lanpeng Jia, Haoqiang Fan, and Shuaicheng Liu. 2022 · 2022
Later among the works it cites.
Repaint: Inpainting using denoising diffusion probabilistic models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 11461–11471
Andreas Lugmayr, Martin Danelljan, Andres Romero, Fisher Yu, Radu Timofte, and Luc Van Gool. 2022 · 2022
Later among the works it cites.
Toward Fast, Flexible, and Robust Low-Light Image Enhancement. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 5637–5646
Long Ma, Tengyu Ma, Risheng Liu, Xin Fan, and Zhongxuan Luo. 2022 · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
DSFD: dual shot face detector. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 5060–5069
Jian Li, Yabiao Wang, Changan Wang, Ying Tai, Jianjun Qian, Jian Yang, Chengjie Wang, Jilin Li, and Feiyue Huang. 2019 · 2019
Cited alongside, same era.
Getting to know low-light images with the exclusively dark dataset
Yuen Peng Loh and Chee Seng Chan. 2019 · 2019
Cited alongside, same era.
Generative modeling by estimating gradients of the data distribution
Yang Song and Stefano Ermon. 2019 · 2019
Cited alongside, same era.
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
Chunle Guo, Chongyi Li, Jichang Guo, Chen Change Loy, Junhui Hou, Sam Kwong, and Runmin Cong. 2020 · 2020
Cited alongside, same era.
Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel. 2020 · 2020
Cited alongside, same era.
DSLR: deep stacked Laplacian restorer for low-light image enhancement
Seokjae Lim and Wonjun Kim. 2020 · 2020
Cited alongside, same era.
Advancing image understanding in poor visibility environments: A collective benchmark study
Wenhan Yang, Ye Yuan, Wenqi Ren, Jiaying Liu, Walter J Scheirer, Zhangyang Wang, Taiheng Zhang, Qiaoyong Zhong, Di Xie, Shiliang Pu, et al · 2020
Cited alongside, same era.
Jaemin Park, An Gia Vien, Jin-Hwan Kim, and Chul Lee. 2022 · 2022
Later among the works it cites.
Image Deblurring with Domain Generalizable Diffusion Models
Mengwei Ren, Mauricio Delbracio, Hossein Talebi, Guido Gerig, and Peyman Milanfar. 2022 · 2022
Later among the works it cites.
High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 10684–10695
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. 2022 · 2022
Later among the works it cites.
Palette: Image-to-image diffusion models. In ACM SIGGRAPH 2022 Conference Proceedings . 1–10
Chitwan Saharia, William Chan, Huiwen Chang, Chris Lee, Jonathan Ho, Tim Salimans, David Fleet, and Mohammad Norouzi. 2022 · 2022
Later among the works it cites.
Uformer: A general u-shaped transformer for image restoration. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 17683–17693
Zhendong Wang, Xiaodong Cun, Jianmin Bao, Wengang Zhou, Jianzhuang Liu, and Houqiang Li. 2022 · 2022
Later among the works it cites.
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
Wenhui Wu, Jian Weng, Pingping Zhang, Xu Wang, Wenhan Yang, and Jianmin Jiang. 2022 · 2022
Later among the works it cites.
SNR-aware low-light image enhancement. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 17714–17724
Xiaogang Xu, Ruixing Wang, Chi-Wing Fu, and Jiaya Jia. 2022 · 2022
Later among the works it cites.
Rethinking low-light enhancement via transformer-GAN
Shaoliang Yang, Dongming Zhou, Jinde Cao, and Yanbu Guo. 2022 · 2022
Later among the works it cites.
Restormer: Efficient transformer for high-resolution image restoration. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 5728–5739
Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Ming-Hsuan Yang. 2022 · 2022
Later among the works it cites.
Learn from unpaired data for image restoration: A variational bayes approach
Dihan Zheng, Xiaowen Zhang, Kaisheng Ma, and Chenglong Bao. 2022 · 2022
Later among the works it cites.
Generative Diffusion Prior for Unified Image Restoration and Enhancement. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 9935–9946
Ben Fei, Zhaoyang Lyu, Liang Pan, Junzhe Zhang, Weidong Yang, Tianyue Luo, Bo Zhang, and Bo Dai. 2023 · 2023
Closest in time.
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
Lanqing Guo, Chong Wang, Wenhan Yang, Siyu Huang, Yufei Wang, Hanspeter Pfister, and Bihan Wen. 2023 · 2023
Closest in time.
R2rnet: Low-light image enhancement via real-low to real-normal network
Jiang Hai, Zhu Xuan, Ren Yang, Yutong Hao, Fengzhu Zou, Fang Lin, and Songchen Han. 2023 · 2023
Closest in time.
Embedding Fourier for Ultra-High-Definition Low-Light Image Enhancement. In International Conference on Learning Representations
Chongyi Li, Chun-Le Guo, Man Zhou, Zhexin Liang, Shangchen Zhou, Ruicheng Feng, and Chen Change Loy. 2023 · 2023
Closest in time.
Image Restoration with Mean-Reverting Stochastic Differential Equations
Ziwei Luo, Fredrik K Gustafsson, Zheng Zhao, Jens Sjölund, and Thomas B Schön. 2023a · 2023
Closest in time.
Restoring vision in adverse weather conditions with patch-based denoising diffusion models
Ozan Özdenizci and Robert Legenstein. 2023 · 2023
Closest in time.
ResDiff: Combining CNN and Diffusion Model for Image Super-Resolution
Shuyao Shang, Zhengyang Shan, Guangxing Liu, and Jinglin Zhang. 2023 · 2023
Closest in time.
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
Zhixin Wang, Xiaoyun Zhang, Ziying Zhang, Huangjie Zheng, Mingyuan Zhou, Ya Zhang, and Yanfeng Wang. 2023 · 2023
Closest in time.
Sparse gradient regularized deep retinex network for robust low-light image enhancement
Wenhan Yang, Wenjing Wang, Haofeng Huang, Shiqi Wang, and Jiaying Liu. 2021b · 2086
Closest in time.
Image denoising by sparse 3-D transform-domain collaborative filtering
Kostadin Dabov, Alessandro Foi, Vladimir Katkovnik, and Karen Egiazarian. 2007 · 2095
Closest in time.