Fetching the paper…
Reading the bibliography…
Despite the tremendous success of deep models in various individual image restoration tasks, there are at least two major technical challenges preventing these works from being applied to real-world usages: (1) the lack of generalization ability and (2) the complex and unknown degradations in real-world scenarios.
Bayesian-Based Iterative Method of Image Restoration
William Hadley Richardson · 1972
Earlier work this paper cites.
A dendrite method for cluster analysis
Tadeusz Caliński and Jerzy Harabasz · 1974
Earlier work this paper cites.
An iterative technique for the rectification of observed distributions
Leon B Lucy · 1974
Earlier work this paper cites.
Image inpainting
Marcelo Bertalmio, Guillermo Sapiro, Vincent Caselles, and Coloma Ballester · 2000
Earlier work this paper cites.
On spectral clustering: Analysis and an algorithm
Andrew Ng, Michael Jordan, and Yair Weiss · 2001
Earlier work this paper cites.
Image quality assessment: from error visibility to structural similarity
Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli · 2004
Earlier work this paper cites.
A tutorial on spectral clustering
Ulrike Von Luxburg · 2007
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
Online object tracking: A benchmark
Yi Wu, Jongwoo Lim, and Ming-Hsuan Yang · 2013
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
Compression artifacts reduction by a deep convolutional network
Chao Dong, Yubin Deng, Chen Change Loy, and Xiaoou Tang · 2015
Earlier work this paper cites.
Image super-resolution using deep convolutional networks
Chao Dong, Chen Change Loy, Kaiming He, and Xiaoou Tang · 2015
Earlier work this paper cites.
Dehazenet: An end-to-end system for single image haze removal
Bolun Cai, Xiangmin Xu, Kui Jia, Chunmei Qing, and Dacheng Tao · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Learning depth from single monocular images using deep convolutional neural fields
Fayao Liu, Chunhua Shen, Guosheng Lin, and Ian Reid · 2016
Earlier work this paper cites.
Context encoders: Feature learning by inpainting
Deepak Pathak, Philipp Krahenbuhl, Jeff Donahue, Trevor Darrell, and Alexei A Efros · 2016
Earlier work this paper cites.
Ntire 2017 challenge on single image super-resolution: Dataset and study
Eirikur Agustsson and Radu Timofte · 2017
Earlier work this paper cites.
Clearing the skies: A deep network architecture for single-image rain removal
Xueyang Fu, Jiabin Huang, Xinghao Ding, Yinghao Liao, and John Paisley · 2017
Earlier work this paper cites.
Photo-realistic single image super-resolution using a generative adversarial network
Christian Ledig, Lucas Theis, Ferenc Huszár, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, et al · 2017
Earlier work this paper cites.
Enhanced deep residual networks for single image super-resolution
Bee Lim, Sanghyun Son, Heewon Kim, Seungjun Nah, and Kyoung Mu Lee · 2017
Earlier work this paper cites.
Deep multi-scale convolutional neural network for dynamic scene deblurring
Seungjun Nah, Tae Hyun Kim, and Kyoung Mu Lee · 2017
Earlier work this paper cites.
Automatic differentiation in pytorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
Earlier work this paper cites.
Revisiting unreasonable effectiveness of data in deep learning era
Chen Sun, Abhinav Shrivastava, Saurabh Singh, and Abhinav Gupta · 2017
Earlier work this paper cites.
Ntire 2017 challenge on single image super-resolution: Methods and results
Radu Timofte, Eirikur Agustsson, Luc Van Gool, Ming-Hsuan Yang, and Lei Zhang · 2017
Earlier work this paper cites.
Deep joint rain detection and removal from a single image
Wenhan Yang, Robby T Tan, Jiashi Feng, Jiaying Liu, Zongming Guo, and Shuicheng Yan · 2017
Earlier work this paper cites.
Deep joint rain detection and removal from a single image
Wenhan Yang, Robby T Tan, Jiashi Feng, Jiaying Liu, Zongming Guo, and Shuicheng Yan · 2017
Earlier work this paper cites.
Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising
Kai Zhang, Wangmeng Zuo, Yunjin Chen, Deyu Meng, and Lei Zhang · 2017
Earlier work this paper cites.
A high-quality denoising dataset for smartphone cameras
Abdelrahman Abdelhamed, Stephen Lin, and Michael S Brown · 2018
Earlier work this paper cites.
Deblurgan: Blind motion deblurring using conditional adversarial networks
Orest Kupyn, Volodymyr Budzan, Mykola Mykhailych, Dmytro Mishkin, and Jiří Matas · 2018
Earlier work this paper cites.
Desnownet: Context-aware deep network for snow removal
Yun-Fu Liu, Da-Wei Jaw, Shih-Chia Huang, and Jenq-Neng Hwang · 2018
Earlier work this paper cites.
Desnownet: Context-aware deep network for snow removal
Yun-Fu Liu, Da-Wei Jaw, Shih-Chia Huang, and Jenq-Neng Hwang · 2018
Earlier work this paper cites.
Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever, et al · 2018
Earlier work this paper cites.
Multi-task learning as multi-objective optimization
Ozan Sener and Vladlen Koltun · 2018
Earlier work this paper cites.
Scale-recurrent network for deep image deblurring
Xin Tao, Hongyun Gao, Xiaoyong Shen, Jue Wang, and Jiaya Jia · 2018
Earlier work this paper cites.
Esrgan: Enhanced super-resolution generative adversarial networks
Xintao Wang, Ke Yu, Shixiang Wu, Jinjin Gu, Yihao Liu, Chao Dong, Yu Qiao, and Chen Change Loy · 2018
Earlier work this paper cites.
Density-aware single image de-raining using a multi-stream dense network
He Zhang and Vishal M Patel · 2018
Earlier work this paper cites.
Density-aware single image de-raining using a multi-stream dense network
He Zhang and Vishal M Patel · 2018
Earlier work this paper cites.
Ffdnet: Toward a fast and flexible solution for cnn-based image denoising
Kai Zhang, Wangmeng Zuo, and Lei Zhang · 2018
Earlier work this paper cites.
Learning a single convolutional super-resolution network for multiple degradations
Kai Zhang, Wangmeng Zuo, and Lei Zhang · 2018
Earlier work this paper cites.
Toward real-world single image super-resolution: A new benchmark and a new model
Jianrui Cai, Hui Zeng, Hongwei Yong, Zisheng Cao, and Lei Zhang · 2019
Earlier work this paper cites.
Blind super-resolution with iterative kernel correction
Jinjin Gu, Hannan Lu, Wangmeng Zuo, and Chao Dong · 2019
Earlier work this paper cites.
Blind super-resolution with iterative kernel correction
Jinjin Gu, Hannan Lu, Wangmeng Zuo, and Chao Dong · 2019
Earlier work this paper cites.
Modulating image restoration with continual levels via adaptive feature modification layers
Jingwen He, Chao Dong, and Yu Qiao · 2019
Earlier work this paper cites.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin Ming-Wei Chang Kenton and Lee Kristina Toutanova · 2019
Cited alongside, same era.
Benchmarking single-image dehazing and beyond
Boyi Li, Wenqi Ren, Dengpan Fu, Dacheng Tao, Dan Feng, Wenjun Zeng, and Zhangyang Wang · 2019
Cited alongside, same era.
Griddehazenet: Attention-based multi-scale network for image dehazing
Xiaohong Liu, Yongrui Ma, Zhihao Shi, and Jun Chen · 2019
Cited alongside, same era.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al · 2019
Cited alongside, same era.
Progressive image deraining networks: A better and simpler baseline
Dongwei Ren, Wangmeng Zuo, Qinghua Hu, Pengfei Zhu, and Deyu Meng · 2019
Cited alongside, same era.
Spatial attentive single-image deraining with a high quality real rain dataset
Simple baselines for image restoration
Liangyu Chen, Xiaojie Chu, Xiangyu Zhang, and Jian Sun · 2022
Later among the works it cites.
Learning multiple adverse weather removal via two-stage knowledge learning and multi-contrastive regularization: Toward a unified model
Wei-Ting Chen, Zhi-Kai Huang, Cheng-Che Tsai, Hao-Hsiang Yang, Jian-Jiun Ding, and Sy-Yen Kuo · 2022
Later among the works it cites.
Activating more pixels in image super-resolution transformer
Xiangyu Chen, Xintao Wang, Jiantao Zhou, and Chao Dong · 2022
Later among the works it cites.
Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision
Priya Goyal, Quentin Duval, Isaac Seessel, Mathilde Caron, Ishan Misra, Levent Sagun, Armand Joulin, and Piotr Bojanowski · 2022
Later among the works it cites.
Masked autoencoders are scalable vision learners
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Tianyu Wang, Xin Yang, Ke Xu, Shaozhe Chen, Qiang Zhang, and Rynson WH Lau · 2019
Cited alongside, same era.
Path-restore: Learning network path selection for image restoration
Ke Yu, Xintao Wang, Chao Dong, Xiaoou Tang, and Chen Change Loy · 2019
Cited alongside, same era.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
Cited alongside, same era.
A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
Cited alongside, same era.
Physics-based feature dehazing networks
Jiangxin Dong and Jinshan Pan · 2020
Cited alongside, same era.
Bootstrap your own latent-a new approach to self-supervised learning
Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, et al · 2020
Cited alongside, same era.
Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
Cited alongside, same era.
Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick · 2022
Later among the works it cites.
Reflash dropout in image super-resolution
Xiangtao Kong, Xina Liu, Jinjin Gu, Yu Qiao, and Chao Dong · 2022
Later among the works it cites.
All-In-One Image Restoration for Unknown Corruption
Boyun Li, Xiao Liu, Peng Hu, Zhongqin Wu, Jiancheng Lv, and Xi Peng · 2022
Later among the works it cites.
Efficient and degradation-adaptive network for real-world image super-resolution
Jie Liang, Hui Zeng, and Lei Zhang · 2022
Later among the works it cites.
Blind image super-resolution: A survey and beyond
Anran Liu, Yihao Liu, Jinjin Gu, Yu Qiao, and Chao Dong · 2022
Later among the works it cites.
Tape: Task-agnostic prior embedding for image restoration
Lin Liu, Lingxi Xie, Xiaopeng Zhang, Shanxin Yuan, Xiangyu Chen, Wengang Zhou, Houqiang Li, and Qi Tian · 2022
Later among the works it cites.
Evaluating the generalization ability of super-resolution networks
Yihao Liu, Hengyuan Zhao, Jinjin Gu, Yu Qiao, and Chao Dong · 2022
Later among the works it cites.
High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
Later among the works it cites.
Vision transformers for single image dehazing
Yuda Song, Zhuqing He, Hui Qian, and Xin Du · 2022
Later among the works it cites.
Uformer: A general u-shaped transformer for image restoration
Zhendong Wang, Xiaodong Cun, Jianmin Bao, Wengang Zhou, Jianzhuang Liu, and Houqiang Li · 2022
Later among the works it cites.
Restormer: Efficient transformer for high-resolution image restoration
Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Ming-Hsuan Yang · 2022
Later among the works it cites.
Scaling vision transformers
Xiaohua Zhai, Alexander Kolesnikov, Neil Houlsby, and Lucas Beyer · 2022
Later among the works it cites.
Scaling vision transformers
Xiaohua Zhai, Alexander Kolesnikov, Neil Houlsby, and Lucas Beyer · 2022
Later among the works it cites.
Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al · 2023
Later among the works it cites.
Masked image training for generalizable deep image denoising
Haoyu Chen, Jinjin Gu, Yihao Liu, Salma Abdel Magid, Chao Dong, Qiong Wang, Hanspeter Pfister, and Lei Zhu · 2023
Later among the works it cites.
A comparative study of image restoration networks for general backbone network design
Xiangyu Chen, Zheyuan Li, Yuandong Pu, Yihao Liu, Jiantao Zhou, Yu Qiao, and Chao Dong · 2023
Later among the works it cites.
Dual aggregation transformer for image super-resolution
Zheng Chen, Yulun Zhang, Jinjin Gu, Linghe Kong, Xiaokang Yang, and Fisher Yu · 2023
Later among the works it cites.
Networks are slacking off: Understanding generalization problem in image deraining
Jinjin Gu, Xianzheng Ma, Xiangtao Kong, Yu Qiao, and Chao Dong · 2023
Later among the works it cites.
Prompt-in-prompt learning for universal image restoration
Zilong Li, Yiming Lei, Chenglong Ma, Junping Zhang, and Hongming Shan · 2023
Later among the works it cites.
Diffbir: Towards blind image restoration with generative diffusion prior
Xinqi Lin, Jingwen He, Ziyan Chen, Zhaoyang Lyu, Ben Fei, Bo Dai, Wanli Ouyang, Yu Qiao, and Chao Dong · 2023
Later among the works it cites.
Unifying image processing as visual prompting question answering
Yihao Liu, Xiangyu Chen, Xianzheng Ma, Xintao Wang, Jiantao Zhou, Yu Qiao, and Chao Dong · 2023
Later among the works it cites.
Degae: A new pretraining paradigm for low-level vision
Yihao Liu, Jingwen He, Jinjin Gu, Xiangtao Kong, Yu Qiao, and Chao Dong · 2023
Later among the works it cites.
Prores: Exploring degradation-aware visual prompt for universal image restoration
Jiaqi Ma, Tianheng Cheng, Guoli Wang, Qian Zhang, Xinggang Wang, and Lefei Zhang · 2023
Later among the works it cites.
Promptir: Prompting for all-in-one blind image restoration
Vaishnav Potlapalli, Syed Waqas Zamir, Salman Khan, and Fahad Shahbaz Khan · 2023
Later among the works it cites.
Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al · 2023
Later among the works it cites.
Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al · 2023
Later among the works it cites.
Pixel-aware stable diffusion for realistic image super-resolution and personalized stylization
Tao Yang, Rongyuan Wu, Peiran Ren, Xuansong Xie, and Lei Zhang · 2023
Later among the works it cites.
Ingredient-oriented multi-degradation learning for image restoration
Jinghao Zhang, Jie Huang, Mingde Yao, Zizheng Yang, Hu Yu, Man Zhou, and Feng Zhao · 2023
Later among the works it cites.
Crafting training degradation distribution for the accuracy-generalization trade-off
Ruofan Zhang, Jinjin Gu, Haoyu Chen, Chao Dong, Yulun Zhang, and Wenming Yang · 2023
Later among the works it cites.
Seal: A framework for systematic evaluation of real-world super-resolution
Wenlong Zhang, Xiaohui Li, Xiangyu Chen, Xiaoyun Zhang, Yu Qiao, Xiao-Ming Wu, and Chao Dong · 2023
Later among the works it cites.
Zheng Cai, Maosong Cao, Haojiong Chen, Kai Chen, Keyu Chen, Xin Chen, Xun Chen, Zehui Chen, Zhi Chen, Pei Chu, et al · 2024
Closest in time.
Learning a low-level vision generalist via visual task prompt
Xiangyu Chen, Yihao Liu, Yuandong Pu, Wenlong Zhang, Jiantao Zhou, Yu Qiao, and Chao Dong · 2024
Closest in time.
Towards effective multiple-in-one image restoration: A sequential and prompt learning strategy
Xiangtao Kong, Chao Dong, and Lei Zhang · 2024
Closest in time.
Improved baselines with visual instruction tuning
Haotian Liu, Chunyuan Li, Yuheng Li, and Yong Jae Lee · 2024
Closest in time.
Visual instruction tuning
Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee · 2024
Closest in time.
Exploiting diffusion prior for real-world image super-resolution
Jianyi Wang, Zongsheng Yue, Shangchen Zhou, Kelvin CK Chan, and Chen Change Loy · 2024
Closest in time.
Seesr: Towards semantics-aware real-world image super-resolution
Rongyuan Wu, Tao Yang, Lingchen Sun, Zhengqiang Zhang, Shuai Li, and Lei Zhang · 2024
Closest in time.
Scaling up to excellence: Practicing model scaling for photo-realistic image restoration in the wild
Fanghua Yu, Jinjin Gu, Zheyuan Li, Jinfan Hu, Xiangtao Kong, Xintao Wang, Jingwen He, Yu Qiao, and Chao Dong · 2024
Closest in time.
Real-world image super-resolution as multi-task learning
Wenlong Zhang, Xiaohui Li, Guangyuan Shi, Xiangyu Chen, Yu Qiao, Xiaoyun Zhang, Xiao-Ming Wu, and Chao Dong · 2024
Closest in time.