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The Mamba-based image restoration backbones have recently demonstrated significant potential in balancing global reception and computational efficiency.
Two deterministic half-quadratic regularization algorithms for computed imaging
Pierre Charbonnier, Laure Blanc-Feraud, Gilles Aubert, and Michel Barlaud · 1994
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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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LIVE image quality assessment database release 2
H Sheikh · 2005
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Pointwise shape-adaptive DCT for high-quality denoising and deblocking of grayscale and color images
Alessandro Foi, Vladimir Katkovnik, and Karen Egiazarian · 2007
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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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Compression artifacts reduction by a deep convolutional network
Chao Dong, Yubin Deng, Chen Change Loy, and Xiaoou Tang · 2015
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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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Categorical reparameterization with gumbel-softmax
Eric Jang, Shixiang Gu, and Ben Poole · 2016
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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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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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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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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Fast, accurate, and lightweight super-resolution with cascading residual network
Namhyuk Ahn, Byungkon Kang, and Kyung-Ah Sohn · 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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Residual non-local attention networks for image restoration
Yulun Zhang, Kunpeng Li, Kai Li, Bineng Zhong, and Yun Fu · 2019
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2020
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Transformers are RNNs: Fast autoregressive transformers with linear attention
Angelos Katharopoulos, Apoorv Vyas, Nikolaos Pappas, and François Fleuret · 2020
Vision transformers in image restoration: A survey
Anas M Ali, Bilel Benjdira, Anis Koubaa, Walid El-Shafai, Zahid Khan, and Wadii Boulila · 2023
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Mamba: Linear-time sequence modeling with selective state spaces
Albert Gu and Tri Dao · 2023
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Efficient and explicit modelling of image hierarchies for image restoration
Yawei Li, Yuchen Fan, Xiaoyu Xiang, Denis Demandolx, Rakesh Ranjan, Radu Timofte, and Luc Van Gool · 2023
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Omni aggregation networks for lightweight image super-resolution
Hang Wang, Xuanhong Chen, Bingbing Ni, Yutian Liu, and Jinfan Liu · 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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Latticenet: Towards lightweight image super-resolution with lattice block
Xiaotong Luo, Yuan Xie, Yulun Zhang, Yanyun Qu, Cuihua Li, and Yun Fu · 2020
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Residual dense network for image restoration
Yulun Zhang, Yapeng Tian, Yu Kong, Bineng Zhong, and Yun Fu · 2020
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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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Conditional positional encodings for vision transformers
Xiangxiang Chu, Zhi Tian, Bo Zhang, Xinlong Wang, and Chunhua Shen · 2021
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Kodak lossless true color image suite
Rich Franzen · 2021
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Interpreting super-resolution networks with local attribution maps
Jinjin Gu and Chao Dong · 2021
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On efficient transformer-based image pre-training for low-level vision
Wenbo Li, Xin Lu, Shengju Qian, Jiangbo Lu, Xiangyu Zhang, and Jiaya Jia · 2021
Cited alongside, same era.
RetinexMamba: Retinex-based Mamba for low-light image enhancement
Jiesong Bai, Yuhao Yin, Qiyuan He, Yuanxian Li, and Xiaofeng Zhang · 2024
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Learning enriched features via selective state spaces model for efficient image deblurring
Hu Gao, Bowen Ma, Ying Zhang, Jingfan Yang, Jing Yang, and Depeng Dang · 2024
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Demystify Mamba in vision: A linear attention perspective
Dongchen Han, Ziyi Wang, Zhuofan Xia, Yizeng Han, Yifan Pu, Chunjiang Ge, Jun Song, Shiji Song, Bo Zheng, and Gao Huang · 2024
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A survey on all-in-one image restoration: Taxonomy, evaluation and future trends
Junjun Jiang, Zengyuan Zuo, Gang Wu, Kui Jiang, and Xianming Liu · 2024
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Wei-Tung Lin, Yong-Xiang Lin, Jyun-Wei Chen, and Kai-Lung Hua · 2024
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Hi-Mamba: Hierarchical Mamba for efficient image super-resolution
Junbo Qiao, Jincheng Liao, Wei Li, Yulun Zhang, Yong Guo, Yi Wen, Zhangxizi Qiu, Jiao Xie, Jie Hu, and Shaohui Lin · 2024
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MambaVC: Learned visual compression with selective state spaces
Shiyu Qin, Jinpeng Wang, Yimin Zhou, Bin Chen, Tianci Luo, Baoyi An, Tao Dai, Shutao Xia, and Yaowei Wang · 2024
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VmambaIR: Visual state space model for image restoration
Yuan Shi, Bin Xia, Xiaoyu Jin, Xing Wang, Tianyu Zhao, Xin Xia, Xuefeng Xiao, and Wenming Yang · 2024
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MambaLLIE: Implicit retinex-aware low light enhancement with global-then-local state space
Jiangwei Weng, Zhiqiang Yan, Ying Tai, Jianjun Qian, Jian Yang, and Jun Li · 2024
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RainMamba: Enhanced locality learning with state space models for video deraining
Hongtao Wu, Yijun Yang, Huihui Xu, Weiming Wang, Jinni Zhou, and Lei Zhu · 2024
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FusionMamba: Dynamic feature enhancement for multimodal image fusion with Mamba
Xinyu Xie, Yawen Cui, Chio-In Ieong, Tao Tan, Xiaozhi Zhang, Xubin Zheng, and Zitong Yu · 2024
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FreqMamba: Viewing Mamba from a frequency perspective for image deraining
Zou Zhen, Yu Hu, and Zhao Feng · 2024
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U-shaped vision Mamba for single image dehazing
Zhuoran Zheng and Chen Wu · 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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Diffusion models in low-level vision: A survey
Chunming He, Yuqi Shen, Chengyu Fang, Fengyang Xiao, Longxiang Tang, Yulun Zhang, Wangmeng Zuo, Zhenhua Guo, and Xiu Li · 2025
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