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Table of Contents

Image Super Resolution

  • 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, Chao Dong
    [arXiv 2308] [Project] [Pytorch-Code]
    [DiffBIR] 🔥

  • Exploiting Diffusion Prior for Real-World Image Super-Resolution
    Jianyi Wang, Zongsheng Yue, Shangchen Zhou, Kelvin C.K. Chan, Chen Change Loy
    [arXiv 2305] [Project] [Pytorch-Code]
    [StableSR] 🔥

  • Lightweight Image Super-Resolution with Superpixel Token Interaction
    Aiping Zhang, Wenqi Ren, Yi Liu, Xiaochun Cao
    [ICCV 2023] [Pytorch-Code]
    [SPIN]

  • SRFormer: Permuted Self-Attention for Single Image Super-Resolution
    Yupeng Zhou, Zhen Li, Chun-Le Guo, Song Bai, Ming-Ming Cheng, Qibin Hou
    [ICCV 2023] [Pytorch-Code]

  • Dual Aggregation Transformer for Image Super-Resolution
    Zheng Chen, Yulun Zhang, Jinjin Gu, Linghe Kong, Xiaokang Yang, Fisher Yu
    [ICCV 2023] [Pytorch-Code]
    [DAT]

  • Spatially-Adaptive Feature Modulation for Efficient Image Super-Resolution
    Long Sun, Jiangxin Dong, Jinhui Tang, Jinshan Pan
    [ICCV 2023] [Project] [Pytorch-Code]
    [SAFMN]

  • DLGSANet: Lightweight Dynamic Local and Global Self-Attention Networks for Image Super-Resolutionn
    Xiang Li, Jinshan Pan, Jinhui Tang, Jiangxin Dong
    [ICCV 2023] [Pytorch-Code]
    [SAFMN]

  • QuickSRNet: Plain Single-Image Super-Resolution Architecture for Faster Inference on Mobile Platforms
    Guillaume Berger, Manik Dhingra, Antoine Mercier, Yashesh Savani, Sunny Panchal, Fatih Porikli
    [CVPRW 2023]
    [★☆] 高通, 轻量级SR, 去掉残差模块

  • DeSRA: Detect and Delete the Artifacts of GAN-based Real-World Super-Resolution Models
    Liangbin Xie, Xintao Wang, Xiangyu Chen, Gen Li, Ying Shan, Jiantao Zhou, Chao Dong
    [ICLR 2023] [Pytorch-Code]
    [DeSRA]

  • Activating More Pixels in Image Super-Resolution Transformer
    Xiangyu Chen, Xintao Wang, Jiantao Zhou, Chao Dong
    [CVPR 2023] [Pytorch-Code]
    [HAT] 🔥 提出了OCAB(Overlapping Cross-Attention Block), 增强了窗口间的信息交互. 与自注意力, 通道注意力一起使用, 设计的网络实现了sota.

  • Simple Baselines for Image Restoration
    Liangyu Chen, Xiaojie Chu, Xiangyu Zhang, Jian Sun
    [arXiv 2204] [Pytorch-Code]
    [NAFNet]

  • Efficient Long-Range Attention Network for Image Super-resolution
    Xindong Zhang, Hui Zeng, Shi Guo, Lei Zhang
    [arXiv 2203] [Pytorch-Code]
    [ELAN]

  • Revisiting RCAN: Improved Training for Image Super-Resolution
    Zudi Lin, Prateek Garg, Atmadeep Banerjee, Salma Abdel Magid, Deqing Sun, Yulun Zhang, Luc Van Gool, Donglai Wei, Hanspeter Pfister
    [arXiv 2201] [Pytorch-Code]

  • Fast Nearest Convolution for Real-Time Efficient Image Super-Resolution
    Ziwei Luo, Youwei Li, Lei Yu, Qi Wu, Zhihong Wen, Haoqiang Fan, Shuaicheng Liu
    [ECCVW 2022] [Pytorch-Code]

  • Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration
    Marcos V. Conde, Ui-Jin Choi, Maxime Burchi, Radu Timofte
    [ECCVW 2022] [Pytorch-Code]

  • From Face to Natural Image: Learning Real Degradation for Blind Image Super-Resolution
    Xiaoming Li, Chaofeng Chen, Xianhui Lin, Wangmeng Zuo, Lei Zhang
    [ECCV 2022] [Pytorch-Code]
    [ReDegNet]

  • Self-Supervised Learning for Real-World Super-Resolution from Dual Zoomed Observations
    Zhilu Zhang, Ruohao Wang, Hongzhi Zhang, Yunjin Chen, Wangmeng Zuo
    [ECCV 2022] [Pytorch-Code]
    [SelfDZSR]

  • Efficient and Degradation-Adaptive Network for Real-World Image Super-Resolution
    Jie Liang, Hui Zeng, Shi Guo, Lei Zhang
    [ECCV 2022] [Pytorch-Code]
    [DASR]

  • Efficient Long-Range Attention Network for Image Super-resolution
    Xindong Zhang, Hui Zeng, Shi Guo, Lei Zhang
    [ECCV 2022] [Pytorch-Code]
    [ELAN]

  • Learning Series-Parallel Lookup Tables for Efficient Image Super-Resolution
    Cheng Ma, Jingyi Zhang, Jie Zhou, Jiwen Lu
    [ECCV 2022] [Pytorch-Code]
    [SPLUT]

  • MuLUT: Cooperating Mulitple Look-Up Tables for Efficient Image Super-Resolution
    Jiacheng Li, Chang Chen, Zhen Cheng, Zhiwei Xiong
    [ECCV 2022] [Project] [Pytorch-Code]

  • Adaptive Patch Exiting for Scalable Single Image Super-Resolution
    Shizun Wang, Jiaming Liu, Kaixin Chen, Xiaoqi Li, Ming Lu, Yandong Guo
    [ECCV 2022 Oral] [Pytorch-Code]
    [APE]

  • Reference-based Image Super-Resolution with Deformable Attention Transformer
    Jiezhang Cao, Jingyun Liang, Kai Zhang, Yawei Li, Yulun Zhang, Wenguan Wang, Luc Van Gool
    [ECCV 2022] [Pytorch-Code]
    [DATSR]

  • Super-Resolution by Predicting Offsets: An Ultra-Efficient Super-Resolution Network for Rasterized Images
    Jinjin Gu, Haoming Cai, Chenyu Dong, Ruofan Zhang, Yulun Zhang, Wenming Yang, Chun Yuan
    [ECCV 2022] [Pytorch-Code]
    [SRPO]

  • D2C-SR: A Divergence to Convergence Approach for Real-World Image Super-Resolution
    Youwei Li, Haibin Huang, Lanpeng Jia, Haoqiang Fan, Shuaicheng Liu
    [ECCV 2022] [Code]

  • MM-RealSR: Metric Learning based Interactive Modulation for Real-World Super-Resolution
    Chong Mou, Yanze Wu, Xintao Wang, Chao Dong, Jian Zhang, Ying Shan
    [ECCV 2022] [Pytorch-Code]

  • Restore Globally, Refine Locally: A Mask-Guided Scheme to Accelerate Super-Resolution Networks
    Xiaotao Hu, Jun Xu, Shuhang Gu, Ming-Ming Cheng, Li Liu
    [ECCV 2022 Oral] [Pytorch-Code]

  • RepSR: Training Efficient VGG-style Super-Resolution Networks with Structural Re-Parameterization and Batch Normalization
    Xintao Wang, Chao Dong, Ying Shan
    [MM 2022] [Pytorch-Code]

  • Discrete Cosine Transform Network for Guided Depth Map Super-Resolution
    Zixiang Zhao, Jiangshe Zhang, Shuang Xu, Zudi Lin, Hanspeter Pfister
    [CVPR 2022 Oral] [Pytorch-Code]

  • Dual Adversarial Adaptation for Cross-Device Real-World Image Super-Resolution
    Xiaoqian Xu, Pengxu Wei, Weikai Chen, Mingzhi Mao, Liang Lin, Guanbin Li
    [CVPR 2022] [Pytorch-Code]

  • Blind Image Super-resolution with Elaborate Degradation Modeling on Noise and Kernel
    Zongsheng Yue, Qian Zhao, Jianwen Xie, Lei Zhang, Deyu Meng, Kwan-Yee K. Wong
    [CVPR 2022] [Pytorch-Code]

  • Deep Constrained Least Squares for Blind Image Super-Resolution
    Ziwei Luo, Haibin Huang, Lei Yu, Youwei Li, Haoqiang Fan, Shuaicheng Liu
    [CVPR 2022] [Pytorch-Code]

  • Learning the Degradation Distribution for Blind Image Super-Resolution
    Zhengxiong Luo, Yan Huang, Shang Li, Liang Wang, Tieniu Tan
    [CVPR 2022] [Pytorch-Code]

  • Reflash Dropout in Image Super-Resolution
    Xiangtao Kong, Xina Liu, Jinjin Gu, Yu Qiao, Chao Dong
    [CVPR 2022] [Pytorch-Code]

  • Learning To Zoom Inside Camera Imaging Pipeline
    Chengzhou Tang, Yuqiang Yang, Bing Zeng, Ping Tan, Shuaicheng Liu
    [CVPR 2022] [Pytorch-Code]

  • Details or Artifacts: A Locally Discriminative Learning Approach to Realistic Image Super-Resolution
    Jie Liang, Hui Zeng, Lei Zhang
    [CVPR 2022 Oral] [Pytorch-Code]
    [LDL]

  • Learning the Degradation Distribution for Blind Image Super-Resolution
    Zhengxiong Luo, Yan Huang, Shang Li, Liang Wang, Tieniu Tan
    [CVPR 2022] [Pytorch-Code]
    [PDM-SR]

  • Deep Constrained Least Squares for Blind Image Super-Resolution
    Ziwei Luo, Haibin Huang, Lei Yu, Youwei Li, Haoqiang Fan, Shuaicheng Liu
    [CVPR 2022] [Pytorch-Code]
    [DCLS-SR]

  • A Text Attention Network for Spatial Deformation Robust Scene Text Image Super-resolution
    Jianqi Ma, Zhetong Liang, Lei Zhang
    [CVPR 2022] [Pytorch-Code]
    [TATT] OPPO

  • ARM: Any-Time Super-Resolution Method
    Bohong Chen, Mingbao Lin, Kekai Sheng, Mengdan Zhang, Peixian Chen, Ke Li, Liujuan Cao, Rongrong Ji
    [CVPR 2022] [Pytorch-Code]

  • Detail-Preserving Transformer for Light Field Image Super-Resolution
    Shunzhou Wang, Tianfei Zhou, Yao Lu, Huijun Di
    [AAAI 2022] [Pytorch-Code]
    [DPT]

  • Finding Discriminative Filters for Specific Degradations in Blind Super-Resolution
    Liangbin Xie, Xintao Wang, Chao Dong, Zhongang Qi, Ying Shan
    [NIPS 2021 Spotlight] [Pytorch-Code]
    [FAIG]

  • Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data
    Xintao Wang, Liangbie Xie, Chao Dong, Ying Shan
    [arXiv 2107] [Pytorch-Code]

  • GhostSR: Learning Ghost Features for Efficient Image Super-Resolution
    Ying Nie, Kai Han, Zhenhua Liu, An Xiao, Yiping Deng, Chunjing Xu, Yunhe Wang
    [arXiv 2101]
    [★☆] 轻量级超分. 使用pixel shift的思想做超分

  • SplitSR: An End-to-End Approach to Super-Resolution on Mobile Devices
    Xin Liu, Yuang Li, Josh Fromm, Yuntao Wang, Ziheng Jiang, Alex Mariakakis, Shwetak Patel
    [arXiv 2101] [Unofficial-Pytorch-Code]
    [★☆] (轻量级超分) 提出了一个轻量级residual block结构: SplitSRBlock

  • About Edge-oriented Convolution Block for Real-time Super Resolution on Mobile Devices
    Xindong Zhang, Hui Zeng, Lei Zhang
    [MM 2021 Oral] [Pytorch-Code]
    [ECBSR] [★★] 重参数化, 并行卷积模块+sobel/laplacian算子

  • Deep Reparametrization of Multi-Frame Super-Resolution and Denoising
    Goutam Bhat, Martin Danelljan, Fisher Yu, Luc Van Gool, Radu Timofte
    [ICCV 2021 Oral]

  • Designing a Practical Degradation Model for Deep Blind Image Super-Resolution
    Kai Zhang, Jingyun Liang, Luc Van Gool, Radu Timofte
    [ICCV 2021] [Pytorch-Code]
    [BSRGAN] [★★] 通过随机排列的退化(模糊, 下采, 加噪等)生成训练数据, 并在此基础上训练了一个盲超分模型. Novelty貌似不太多, 但比较实用.

  • Mutual Affine Network for Spatially Variant Kernel Estimation in Blind Image Super-Resolution
    Jingyun Liang, Guolei Sun, Kai Zhang, Luc Van Gool, Radu Timofte
    [ICCV 2021] [Pytorch-Code]
    [MANet]

  • Hierarchical Conditional Flow: A Unified Framework for Image Super-Resolution and Image Rescaling
    Jingyun Liang, Andreas Lugmayr, Kai Zhang, Martin Danelljan, Luc Van Gool, Radu Timofte
    [ICCV 2021] [Pytorch-Code]
    [HCFlow]

  • Dual-Camera Super-Resolution with Aligned Attention Modules
    Tengfei Wang, Jiaxin Xie, Wenxiu Sun, Qiong Yan, Qifeng Chen
    [ICCV 2021 Oral] [Project] [Pytorch-Code]
    [DCSR]

  • Learning A Single Network for Scale-Arbitrary Super-Resolution
    Longguang Wang, Yingqian Wang, Zaiping Lin, Jungang Yang, Wei An, Yulan Guo
    [ICCV 2021] [Project] [Pytorch-Code]
    [ArbSR]

  • Fast Bayesian Uncertainty Estimation and reduction of Batch Normalized Single Image Super-Resolution Network
    Aupendu Kar, Prabir Kumar Biswas
    [CVPR 2021] [Project] [Pytorch-Code]

  • Unsupervised Real-World Image Super Resolution via Domain-Distance Aware Training
    Yunxuan Wei, Shuhang Gu, Yawei Li, Longcun Jin
    [CVPR 2021] [Pytorch-Code]
    [DASR]

  • Image Super-Resolution With Non-Local Sparse Attention
    Yiqun Mei, Yuchen Fan, Yuqian Zhou
    [CVPR 2021] [Pytorch-Code]

  • KOALAnet: Blind Super-Resolution using Kernel-Oriented Adaptive Local Adjustment
    Soo Ye Kim, Hyeonjun Sim, [Munchurl Kim]https://www.viclab.kaist.ac.kr/)
    [CVPR 2021] [Pytorch-Code]

  • Tackling the Ill-Posedness of Super-Resolution through Adaptive Target Generation
    Younghyun Jo, [Seoung Wug Oh], Peter Vajda, Seon Joo Kim
    [CVPR 2021] [Pytorch-Code]
    认为在SR任务中, HR和LR位置并不是完全对应的, 因此设计了一个ART模块, 对GT patch进行仿射变换, 使LR和HRpair更加匹配 [AdaTarget] [★]

  • Single Pair Cross-Modality Super Resolution
    Guy Shacht, Dov Danon, Sharon Fogel, Daniel Cohen-Or
    [CVPR 2021]

  • Practical Single-Image Super-Resolution Using Look-Up Table
    Younghyun Jo, Seon Joo Kim
    [CVPR 2021] [Pytorch-Code]
    [SR-LUT]

  • SRFlow-DA: Super-Resolution Using Normalizing Flow with Deep Convolutional Block
    Younghyun Jo, Sejong Yang, Seon Joo Kim
    [CVPRW 2021] [Pytorch-Code]

  • SRWarp: Generalized Image Super-Resolution under Arbitrary Transformation
    Sanghyun Son, Kyoung Mu Lee
    [CVPR 2021] [Pytorch-Code]

  • Interpreting Super-Resolution Networks with Local Attribution Maps
    Jinjin Gu, Chao Dong
    [CVPR 2021] [Project]
    [LAM]

  • Flow-based Kernel Prior with Application to Blind Super-Resolution
    Jingyun Liang, Kai Zhang, Shuhang Gu, Luc Van Gool, Radu Timofte
    [CVPR 2021] [Pytorch-Code]
    [FKP]

  • MASA-SR: Matching Acceleration and Spatial Adaptation for Reference-Based Image Super-Resolution
    Liying Lu, Wenbo Li, Xin Tao, Jiangbo Lu, Jiaya Jia
    [CVPR 2021] [Pytorch-Code]

  • GLEAN: Generative Latent Bank for Large-Factor Image Super-Resolution
    Kelvin C.K. Chan, Xintao Wang, Xiangyu Xu, Jinwei Gu, Chen Change Loy
    [CVPR 2021 Oral] [Project]

  • Learning Continuous Image Representation with Local Implicit Image Function
    Yinbo Chen, Sifei Liu, Xiaolong Wang
    [CVPR 2021 Oral] [Project] [Pytorch-Code]
    [LIIF]

  • LAU-Net: Latitude Adaptive Upscaling Network for Omnidirectional Image Super-resolution
    Yinbo Chen, Sifei Liu, Xiaolong Wang
    [CVPR 2021] [Pytorch-Code]

  • Cross-MPI: Cross-scale Stereo for Image Super-Resolution using Multiplane Images
    Yuemei Zhou, Gaochang Wu, Ying Fu, Kun Li, Yebin Liu
    [CVPR 2021] [Project]

  • ClassSR: A General Framework to Accelerate Super-Resolution Networks by Data Characteristic
    Xiangtao Kong, Hengyuan Zhao, Yu Qiao, Chao Dong
    [CVPR 2021] [Pytorch-Code]

  • Exploring Sparsity in Image Super-Resolution for Efficient Inference
    Longguang Wang, Xiaoyu Dong, Yingqian Wang, Xinyi Ying, Zaiping Lin, Wei An, Yulan Guo
    [CVPR 2021] [Pytorch-Code]
    [SMSR]

  • Robust Reference-based Super-Resolution via C²-Matching
    Yuming Jiang, Kelvin C.K. Chan, Xintao Wang, Chen Change Loy, Ziwei Liu
    [CVPR 2021] [Project] [Pytorch-Code]

  • AdderSR: Towards Energy Efficient Image Super-Resolution
    Dehua Song, Yunhe Wang, Hanting Chen, Chang Xu, Chunjing Xu, Dacheng Tao
    [CVPR 2021]
    [★] 使用加法做SR

  • Data-Free Knowledge Distillation For Image Super-Resolution
    Yiman Zhang, Hanting Chen, Xinghao Chen, Yiping Deng, Chunjing Xu, Yunhe Wang
    [CVPR 2021]

  • Learning the Non-Differentiable Optimization for Blind Super-Resolution
    Zheng Hui, Jie Li, Xiumei Wang, Xinbo Gao
    [CVPR 2021]
    [AMNet]

  • Frequency Consistent Adaptation for RealWorld Super Resolution
    Xiaozhong Ji, Guangpin Tao, Yun Cao, Ying Tai, Tong Lu, Chengjie Wang, Jilin Li, Feiyue Huang
    [AAAI 2021]
    [FCA] [★]

  • Ultra Lightweight Image Super-Resolution with Multi-Attention Layers
    Abdul Muqeet, Jiwon Hwang, Subin Yang, Jung Heum Kang, Yongwoo Kim, Sung-Ho Bae
    [arXiv 2008] [Code]
    [MAFFSRN] [★☆] 轻量级超分网络

  • Residual Feature Distillation Network for Lightweight Image Super-Resolution
    Jie Liu, Jie Tang, Gangshan Wu
    [arXiv 2009] [Pytorch-Code]
    [RFDN] [★☆] AIM2020-ESR冠军方案, 基于IDN提出了几点改善.

  • Lightweight Single-Image Super-Resolution Network with Attentive Auxiliary Feature Learning
    Xuehui Wang, Qing Wang, Yuzhi Zhao, Junchi Yan, Lei Fan, [Long Chen](
    [ACCV 2020] [Project]
    [A2F] [★] 轻量级超分. 使用attention和dense connection思想

  • Investigating loss functions for extreme super-resolution
    Younghyun Jo, Sejong Yang, Seon Joo Kim
    [CVPRW 2020] [Pytorch-Code]

  • Closed-loop Matters: Dual Regression Networks for Single Image Super-Resolution
    Yong Guo, Jian Chen, Jingdong Wang, Qi Chen, Jiezhang Cao, Zeshuai Deng, Yanwu Xu, Mingkui Tan
    [CVPR 2020] [Pytorch-Code]
    [DRN] [★] 训练时引入HR->LR的映射, 对LR图像进行额外的约束. 大致浏览.

  • Deep Unfolding Network for Image Super-Resolution
    Kai Zhang, Luc Van Gool, Radu Timofte
    [CVPR 2020] [Pytorch-Code]
    [USRNet] [★] 大致浏览, 通过变量分解迭代求解, 一部分用FFT直接求解, 一部分用网络训练.

  • Residual Feature Aggregation Network for Image Super-resolution
    Jie Liu, Wenjie Zhang, Yuting Tang, Jie Tang, Gangshan Wu
    [CVPR 2020] [Code]
    [RFANet] [★] 设计了一个残差聚合和attention模块

  • mage Super-Resolution with Cross-Scale Non-Local Attention and Exhaustive Self-Exemplars Mining
    Yiqun Mei, Yuchen Fan, Yuqian Zhou, Lichao Huang, Thomas S. Huang, Humphrey Shi
    [CVPR 2020] [Pytorch-Code]
    [★☆] 设计了LR到降采样LR的attention, 挖掘不同尺度下的non-local相似性

  • TTSR: Learning Texture Transformer Network for Image Super-Resolution
    Fuzhi Yang, Huan Yang, Jianlong Fu, Hongtao Lu, Baining Guo
    [CVPR 2020] [Pytorch-Code]
    [★★] 使用transformer从参考图像获取纹理信息

  • Unpaired Image Super-Resolution using Pseudo-Supervision
    Shunta Maeda
    [CVPR 2020]
    [★] 大致浏览, 类似CycleGAN的结构

  • Structure-Preserving Super Resolution with Gradient Guidance
    Cheng Ma, Yongming Rao, Yean Cheng, Ce Chen, Jiwen Lu, Jie Zhou
    [CVPR 2020] [Pytorch-Code]
    [SPSR] [★] 在常规超分分支外加入一个gradient分支

  • PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models
    Sachit Menon, Alexandru Damian, Shijia Hu, Nikhil Ravi, Cynthia Rudin
    [CVPR 2020] [Pytorch-Code]
    [★★] (无监督SR) 通过在LR空间优化latent vector使生成的HR图像符合LR图像的内容. 思路很有意思.

  • Correction Filter for Single Image Super-Resolution: Robustifying Off-the-Shelf Deep Super-Resolvers
    Shady Abu Hussein, Tom Tirer, Raja Giryes
    [CVPR 2020 Oral] [Pytorch-Code]

  • Meta-Transfer Learning for Zero-Shot Super-Resolution
    Jae Woong Soh, Sunwoo Cho, Nam Ik Cho
    [CVPR 2020] [TF-Code]
    [MZSR]

  • Rethinking Data Augmentation for Image Super-resolution: A Comprehensive Analysis and a New Strategy
    Jaejun Yoo, Namhyuk Ahn, Kyung-Ah Sohn
    [CVPR 2020] [Pytorch-Code]
    [CutBlur] [★] (数据增强方法) 提出随机置换HR和LR中某一区域, 使网络能够判断哪些区域需要增强, 对过度锐化方面貌似有所改善

  • Guided Frequency Separation Network for Real-World Super-Resolution
    Yuanbo Zhou, Wei Deng, Tong Tong, Qinquan Gao
    [CVPR 2020] [Pytorch-Code]
    [CARB] [★☆] 使用一套基于GAN的无监督方案生成真实LR图像对, 在该方案中提出了所谓颜色引导生成器网络, 用于产生AdaIn中的参数.

  • Real-World Super-Resolution via Kernel Estimation and Noise Injection
    Xiaozhong Ji，Yun Cao，Ying Tai，Chengjie Wang，Jilin Li，Feiyue Huang
    [CVPRW 2020] [Pytorch-Code]
    [REAL-SR] [★★] 设计了一个退化图像的流程, 通过随机模糊核和注入噪声, 生成接近于真实的样本, 在NTIRE 2020超分竞赛中取得了第一名, 并且在真实数据上表现良好

  • Journey towards tiny perceptual superresolution
    Royson Lee, Łukasz Dudziak, Mohamed Abdelfattah, Stylianos I. Venieris, Hyeji Kim, Hongkai Wen, Nicholas D. Lane
    [ECCV 2020]
    [TPSR] [★]

  • Multi-objective reinforced evolution in mobile neural architecture search
    Xiangxiang Chu, Bo Zhang, Ruijun Xu, Hailong Ma
    [ECCV 2020]
    [MoreMNAS] [★] (NAS, Mobile SR)

  • Component Divide-and-Conquer for Real-World Image Super-Resolution
    Pengxu Wei, Ziwei Xie, Hannan Lu, Zongyuan Zhan, Qixiang Ye, Wangmeng Zuo, Liang Lin Lin
    [ECCV 2020] [Pytorch-Code]
    [CDC]

  • Learning the Super-Resolution Space with Normalizing Flow
    Andreas Lugmayr, Martin Danelljan, Luc Van Gool, Radu Timofte
    [ECCV 2020] [Pytorch-Code]
    [SRFlow]

  • Single Image Super-Resolution via a Holistic Attention Network
    Ben Niu, Weilei Wen, Wenqi Ren, Xiangde Zhang, Lianping Yang, Shuzhen Wang, Kaihao Zhang, Xiaochun Cao, Haifeng Shen
    [ECCV 2020] [Pytorch-Code]
    [HAN]

  • Stochastic Frequency Masking to Improve Super-Resolution and Denoising Networks
    Majed El Helou, Ruofan Zhou, Sabine Süsstrunk
    [ECCV 2020] [Pytorch-Code]
    [SFM] [★] 随机去掉某些频率的信号, 减小degradation-kernel overfitting, 以提升测试集上的性能.

  • VarSR: Variational Super-Resolution Network for Very Low Resolution Images
    Sangeek Hyun, Jae-Pil Heo
    [ECCV 2020]
    [VarSR]

  • Learning with Privileged Information for Efficient Image Super-Resolution
    Junghyup Lee, Dohyung Kim, Wonkyung Lee, Bumbsub Ham
    [ECCV 2020] [Project]
    [Pytorch-Code]
    [PISR] [★] T以HR为输入, 训练一个auto encoder. AE的decoder部分作为S的SR网络. 设计了互信息loss等一系列方法训练.

  • Efficient Super Resolution Using Binarized Neural Network
    Yinglan Ma, Hongyu Xiong, Zhe Hu, Lizhuang Ma
    [ECCV 2020]

  • Towards Content-independent Multi-Reference Super-Resolution: Adaptive Pattern Matching and Feature Aggregation
    Xu Yan, Weibing Zhao, Kun Yuan, Ruimao Zhang, Zhen Li, Shuguang Cui
    [ECCV 2020]

  • Zero-Shot Image Super-Resolution with Depth Guided Internal Degradation Learning
    Xi Cheng, Zhenyong Fu, Jian Yang
    [ECCV 2020]

  • Fast Adaptation to Super-Resolution Networks via Meta-Learning
    Xi Cheng, Zhenyong Fu, Jian Yang
    [ECCV 2020] [TF-Code]
    [MLSR]

  • LatticeNet: Towards Lightweight Image Super-Resolution with Lattice Block
    Xiaotong Luo, Yuan Xie, Yulun Zhang, Yanyun Qu, Cuihua Li, Yun Fu
    [ECCV 2020]
    [★] 轻量级超分

  • Linearly-Assembled Pixel-Adaptive Regression Network for Single Image Super-resolution and Beyond
    Wenbo Li, Kun Zhou, Lu Qi, Nianjuan Jiang, Jiangbo Lu, Jiaya Jia
    [NIPS 2020] [Pytorch-Code]
    [LAPAR] [★☆] 预先定义L个滤波器, 滤波器是不同方向的高斯核和DoG核, 对LR进行bicubic插值, 用一个网络预测滤波器的系数.

  • Cross-Scale Internal Graph Neural Network for Image Super-Resolution
    Shangchen Zhou, Jiawei Zhang, Wangmeng Zuo, Chen Change Loy
    [NIPS 2020] [Pytorch-Code]
    [IGNN]

  • Unfolding the Alternating Optimization for Blind Super Resolution
    Zhengxiong Luo, Yan Huang, Shang Li, Liang Wang, Tieniu Tan
    [NIPS 2020] [Pytorch-Code]
    [DAN]

  • Zoom to Learn, Learn to Zoom
    Xuaner Zhang, Qifeng Chen, Ren Ng, Vladlen Koltun
    [CVPR 2019] [Project] [TF-Code]
    [★★] 提出了考虑空间距离的bilateral contextual loss. 提出了一个由光学zoom图像对组成的raw超分数据集

  • Towards Real Scene Super-Resolution with Raw Images
    Xiangyu Xu, Yongrui Ma, Wenxiu Sun
    [CVPR 2019] [Project]
    [★] 大致浏览, 利用Raw做细节恢复, 用RGB做Color校正.

  • Blind Super-Resolution with Iterative Kernel Correction
    Jinjin Gu, Hannan Lu, Wangmeng Zuo, Chao Dong
    [CVPR 2019] [Project]
    [SFTMD] [★] 1) 粗读, 提出一个基于深度学习的交替预测blur kernel和预测超分结果的模型, 对给定的blur有很好的效果; 2) 文中提出的预测blur kernel并用其辅助超分的思路很有意思, 但对真实图像而言无法获得真实的blur kernel用于训练, 另外论文似乎假设一张图像只有一种blur kernel, 感觉不太合理

  • Camera Lens Super-Resolution
    Chang Chen, Zhiwei Xiong, Xinmei Tian, Zheng-Jun Zha, Feng Wu
    [CVPR 2019] [Code & Data]
    [CameraSR] [★] 文章认为普通的插值退化不能模拟由于焦距-FOV变化带来的退化 (其实这是一个无论从分析上还是工程中都很明显的事实...). 最重要的贡献是提出了一个真实DSLR和手机的数据集, 但是在生成单反数据集时, 貌似没有考虑焦距变化带来的景深变化.

  • Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels
    Kai Zhang, Wangmeng Zuo, Lei Zhang
    [CVPR 2019] [Pytorch-Code] [Pytorch-IR-Toolbox]
    [DPSR] [★★] 1. 把HR到LR的退化解释成bicubic降采样+非盲blur kernel退化+加性高斯白噪声的过程. 2. 将求解过程用HQS变量分裂法分解为去模糊和超分+去噪两步, 第一步在频谱域求闭式解, 避免了模糊现象; 第二步可以使用现有的SR方法, 只需额外加入一噪声level. 采用迭代的形式交替求解. 3. 非盲kernel这个先验其实挺强的, 而且只在生成的数据集上做了实验. 但是实际效果来看, 在真实图像上的效果的确很不错.

  • Image Super-Resolution by Neural Texture Transfer
    Zhifei Zhang, Zhaowen Wang, Zhe Lin, Hairong Qi
    [CVPR 2019] [Project] [TF-Code]
    [SRNTT] [★] (借助参考图的超分) 选出参考图中与当前patch最相近的patch, 再与LR一起处理

  • Blind Super-Resolution Kernel Estimation using an Internal-GAN
    Sefi Bell-Kligler, Assaf Shocher, Michal Irani
    [NIPS 2019 Oral] [Project] [Pytorch-Code]
    [KernelGAN] [★★] 无监督预测降质核并进行超分的方法. 使用若干个现象卷积层的GAN预测降质kernel, 训练的的GAN可以合成一个kernel, 作为该图形的降质核, 网络训练采用LSGAN和若干正则项构成. 预测的模糊核作为ZSSR的降质核, 再无监督地预测超分结果

  • Enhanced Super-Resolution Generative Adversarial Networks
    Xintao Wang, Ke Yu, Shixiang Wu, Jinjin Gu, Yihao Liu, Chao Dong, Yu Qiao, Chen Change Loy
    [ECCV 2018 workshop] [Pytorch-Code]
    [ESRGAN]

  • To learn image super-resolution, use a GAN to learn how to do image degradation first
    Adrian Bulat, Jing Yang, Georgios Tzimiropoulos
    [ECCV 2018] [Pytorch-Code]
    [★★] 使用GAN的无监督学习降质

  • Fast, Accurate, and Lightweight Super-Resolution with Cascading Residual Network
    Namhyuk Ahn, Byungkon Kang, Kyung-Ah Sohn
    [ECCV 2018] [Pytorch-Code]
    [CARN] [★☆] 轻量级超分. 在block内部使用group conv和skip connection, 使用全局和局部的跳连. 在低分辨率上处理, 用PixelShuffle上采样.

  • Image Super-Resolution Using Very Deep Residual Channel Attention Networks
    Yulun Zhang, Kunpeng Li, Kai Li, Lichen Wang, Bineng Zhong, Yun Fu
    [ECCV 2018] [Pytorch-Code]
    [RCAN] [★☆] residual + 通道attention

  • "Zero Shot" Super-Resolution using Deep Internal Learning
    Assaf Shocher, Nadav Cohen, Michal Irani
    [CVPR 2018] [Project] [Pytorch-Code]
    [★☆] DL Zero shot超分较早的一篇, 使用LR内部patch将采样后作为训练输入, 对应的LR patch作为输出, 训练网络. 网络收敛后用来预测LR图像的超分结果.

  • Deep Back-Projection Networks For Super-Resolution
    Muhammad Haris, Greg Shakhnarovich, Norimichi Ukita
    [CVPR 2018] [Project]
    [DBPN] [★★] 提出在神经网络中引入back-projection思想, 引入HR到LR的反馈信息. 这个思路或许可以用在其他形式的网络中, 替代加或乘的特征融合方式.

  • Recovering Realistic Texture in Image Super-resolution by Deep Spatial Feature Transform
    Xintao Wang, Ke Yu, Chao Dong, Chen Change Loy
    [CVPR 2018] [Pytorch-Code]
    [SFTGAN] [★★] (结合语义的超分) 从分割图中提取特征作为超分网络feature的scale和shift

  • Residual Dense Network for Image Super-Resolution
    Yulun Zhang, Yapeng Tian, Yu Kong, Bineng Zhong, Yun Fu
    [CVPR 2018] [Pytorch-Code]
    [RDN] [★☆] 提出Residual Dense Block(RDB)

  • Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
    Christian Ledig, Lucas Theis, Ferenc Huszar, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, Wenzhe Shi
    [CVPR 2017] [TF-Code]
    [SRGAN]

  • Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
    Chao Dong, Chen Change Loy, Xiaoou Tang
    [ECCV 2016] [Project]
    [★☆] SRCNN的加速版本, 在小分辨率上处理，用deconv升分辨率.

Burst Super Resolution