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In recent years, Transformers-based models have made significant progress in the field of image restoration by leveraging their inherent ability to capture complex contextual features.
A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics
David Martin, Charless Fowlkes, Doron Tal, and Jitendra Malik · 2001
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Contour detection and hierarchical image segmentation
Pablo Arbelaez, Michael Maire, Charless Fowlkes, and Jitendra Malik · 2010
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Color demosaicking by local directional interpolation and nonlocal adaptive thresholding
Lei Zhang, Xiaolin Wu, Antoni Buades, and Xin Li · 2011
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Low-complexity single-image super-resolution based on nonnegative neighbor embedding
Marco Bevilacqua, Aline Roumy, Christine Guillemot, and Marie Line Alberi-Morel · 2012
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On single image scale-up using sparse-representations
Roman Zeyde, Michael Elad, and Matan Protter · 2012
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Learning a deep convolutional network for image super-resolution
Chao Dong, Chen Change Loy, Kaiming He, and Xiaoou Tang · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Single image super-resolution from transformed self-exemplars
Jia-Bin Huang, Abhishek Singh, and Narendra Ahuja · 2015
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Just noticeable defocus blur detection and estimation
Jianping Shi, Li Xu, and Jiaya Jia · 2015
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Accurate image super-resolution using very deep convolutional networks
Jiwon Kim, Jung Kwon Lee, and Kyoung Mu Lee · 2016
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Understanding the effective receptive field in deep convolutional neural networks
Wenjie Luo, Yujia Li, Raquel Urtasun, and Richard Zemel · 2016
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Waterloo exploration database: New challenges for image quality assessment models
Kede Ma, Zhengfang Duanmu, Qingbo Wu, Zhou Wang, Hongwei Yong, Hongliang Li, and Lei Zhang · 2016
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Edge-based defocus blur estimation with adaptive scale selection
Ali Karaali and Claudio Rosito Jung · 2017
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Enhanced deep residual networks for single image super-resolution
Bee Lim, Sanghyun Son, Heewon Kim, Seungjun Nah, and Kyoung Mu Lee · 2017
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Sketch-based manga retrieval using manga109 dataset
Yusuke Matsui, Kota Ito, Yuji Aramaki, Azuma Fujimoto, Toru Ogawa, Toshihiko Yamasaki, and Kiyoharu Aizawa · 2017
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Benchmarking denoising algorithms with real photographs
Tobias Plotz and Stefan Roth · 2017
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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
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A high-quality denoising dataset for smartphone cameras
Abdelrahman Abdelhamed, Stephen Lin, and Michael S Brown · 2018
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Second-order attention network for single image super-resolution
Tao Dai, Jianrui Cai, Yongbing Zhang, Shu-Tao Xia, and Lei Zhang · 2019
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Deep defocus map estimation using domain adaptation
Junyong Lee, Sungkil Lee, Sunghyun Cho, and Seungyong Lee · 2019
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Defocus deblurring using dual-pixel data
Abdullah Abuolaim and Michael S Brown · 2020
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Second-order attention network for single image super-resolution
Tao Dai, Jianrui Cai, Yongbing Zhang, Shu-Tao Xia, and Lei Zhang · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy · 2020
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Image super-resolution with cross-scale non-local attention and exhaustive self-exemplars mining
Yiqun Mei, Yuchen Fan, Yuqian Zhou, Lichao Huang, Thomas S Huang, and Honghui Shi · 2020
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Single image super-resolution via a holistic attention network
Multi-stage progressive image restoration
Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, Ming-Hsuan Yang, and Ling Shao · 2021
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Plug-and-play image restoration with deep denoiser prior
Kai Zhang, Yawei Li, Wangmeng Zuo, Lei Zhang, Luc Van Gool, and Radu Timofte · 2021
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Cross aggregation transformer for image restoration
Zheng Chen, Yulun Zhang, Jinjin Gu, Yongbing Zhang, Linghe Kong, and Xin Yuan · 2022
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Scaling up your kernels to 31x31: Revisiting large kernel design in cnns
Xiaohan Ding, Xiangyu Zhang, Jungong Han, and Guiguang Ding · 2022
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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
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Efficient long-range attention network for image super-resolution
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Ben Niu, Weilei Wen, Wenqi Ren, Xiangde Zhang, Lianping Yang, Shuzhen Wang, Kaihao Zhang, Xiaochun Cao, and Haifeng Shen · 2020
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Cross-scale internal graph neural network for image super-resolution
Shangchen Zhou, Jiawei Zhang, Wangmeng Zuo, and Chen Change Loy · 2020
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Learning to reduce defocus blur by realistically modeling dual-pixel data
Abdullah Abuolaim, Mauricio Delbracio, Damien Kelly, Michael S Brown, and Peyman Milanfar · 2021
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Pre-trained image processing transformer
Hanting Chen, Yunhe Wang, Tianyu Guo, Chang Xu, Yiping Deng, Zhenhua Liu, Siwei Ma, Chunjing Xu, Chao Xu, and Wen Gao · 2021
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Twins: Revisiting the design of spatial attention in vision transformers, 2021
Xiangxiang Chu, Zhi Tian, Yuqing Wang, Bo Zhang, Haibing Ren, Xiaolin Wei, Huaxia Xia, and Chunhua Shen · 2021
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Kodak lossless true color image suite
Rich Franzen · 2021
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Iterative filter adaptive network for single image defocus deblurring
Junyong Lee, Hyeongseok Son, Jaesung Rim, Sunghyun Cho, and Seungyong Lee · 2021
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Xindong Zhang, Hui Zeng, Shi Guo, and Lei Zhang · 2022
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Mamba: Linear-time sequence modeling with selective state spaces
Albert Gu and Tri Dao · 2023
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Omni aggregation networks for lightweight image super-resolution
Hang Wang, Xuanhong Chen, Bingbing Ni, Yutian Liu, and Liu jinfan · 2023
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When super-resolution meets camouflaged object detection: A comparison study
Juan Wen, Shupeng Cheng, Peng Xu, Bowen Zhou, Radu Timofte, Weiyan Hou, and Luc Van Gool · 2023
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Accurate image restoration with attention retractable transformer
Jiale Zhang, Yulun Zhang, Jinjin Gu, Yongbing Zhang, Linghe Kong, and Xin Yuan · 2023
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Srformer: Permuted self-attention for single image super-resolution
Yupeng Zhou, Zhen Li, Chun-Le Guo, Song Bai, Ming-Ming Cheng, and Qibin Hou · 2023
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Transformers are SSMs: Generalized models and efficient algorithms through structured state space duality
Tri Dao and Albert Gu · 2024
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Vmamba: Visual state space model
Yue Liu, Yunjie Tian, Yuzhong Zhao, Hongtian Yu, Lingxi Xie, Yaowei Wang, Qixiang Ye, and Yunfan Liu · 2024
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Cfat: Unleashing triangularwindows for image super-resolution
Abhisek Ray, Gaurav Kumar, and Maheshkumar H Kolekar · 2024
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An empirical study of mamba-based language models
Roger Waleffe, Wonmin Byeon, Duncan Riach, Brandon Norick, Vijay Korthikanti, Tri Dao, Albert Gu, Ali Hatamizadeh, Sudhakar Singh, Deepak Narayanan, et al · 2024
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Empowering image recovery_ a multi-attention approach
Juan Wen, Yawei Li, Chao Zhang, Weiyan Hou, Radu Timofte, and Luc Van Gool · 2024
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Transcending the limit of local window: Advanced super-resolution transformer with adaptive token dictionary
Leheng Zhang, Yawei Li, Xingyu Zhou, Xiaorui Zhao, and Shuhang Gu · 2024
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Mambair: A simple baseline for image restoration with state-space model
Hang Guo, Jinmin Li, Tao Dai, Zhihao Ouyang, Xudong Ren, and Shu-Tao Xia · 2025
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