CVPR2020｜图像重建(超分辨率，图像恢复，去雨，去雾，去模糊，去噪等)相关论文汇总（附论文链接/开源代码/解析）

Posted 2021-10-14 等待破茧

tags:

篇首语：本文由小常识网(cha138.com)小编为大家整理，主要介绍了CVPR2020｜图像重建(超分辨率，图像恢复，去雨，去雾，去模糊，去噪等)相关论文汇总（附论文链接/开源代码/解析）相关的知识，希望对你有一定的参考价值。

CVPR2020｜图像重建(超分辨率，图像恢复，去雨，去雾，去模糊，去噪等)相关论文汇总（附论文链接/开源代码/解析）【持续更新】

转载自https://www.cnblogs.com/Kobaayyy/p/13163056.html

整理了下今年CVPR图像重建相关的一些论文，包括超分辨率，图像恢复，去雨，去雾，去模糊，去噪等方向。大家如果觉得有帮助，欢迎点赞和收藏~~
Github地址如下，欢迎star~
https://github.com/Kobaayyy/Awesome-CVPR2020-Image-Reconstruction

CVPR2020的所有论文：CVPR 2020 Open Access Repository
CVPR2020Workshops：CVF Open Access

1.超分辨率

图像超分辨率

PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models

论文：https://arxiv.org/abs/2003.03808
代码：GitHub - adamian98/pulse: PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models
解析：杜克大学提出 AI 算法，拯救渣画质马赛克秒变高清
备注：自监督；GAN；放大像素64倍（暂时是最高倍数）；将生成HR图像对应的LR图像与原图（LR）对比，找到最接近的那张，并反推找到对应的HR图像

Closed-Loop Matters: Dual Regression Networks for Single Image Super-Resolution

论文：https://arxiv.org/abs/2003.07018
代码：GitHub - guoyongcs/DRN: Closed-loop Matters: Dual Regression Networks for Single Image Super-Resolution
解析：
备注：DRN

EventSR: From Asynchronous Events to Image Reconstruction, Restoration, and Super-Resolution via End-to-End Adversarial Learning

作者： Lin Wang, Tae-Kyun Kim, Kuk-Jin Yoon
单位：韩国科学技术院；伦敦帝国学院
论文：http://openaccess.thecvf.com/content_CVPR_2020/papers/Wang_EventSR_From_Asynchronous_Events_to_Image_Reconstruction_Restoration_and_Super-Resolution_CVPR_2020_paper.pdf
视频：https://www.youtube.com/watch?v=OShS_MwHecs
数据集： GitHub - wl082013/ESIM_dataset: Event camera dataset
备注：图像重建、恢复、超分

Unpaired Image Super-Resolution Using Pseudo-Supervision

论文：https://arxiv.org/abs/2002.11397?context=eess
代码：
解析：#每日五分钟一读#Image Super-Resolution
备注：

Correction Filter for Single Image Super-Resolution: Robustifying Off-the-Shelf Deep Super-Resolvers

作者： Shady Abu Hussein, Tom Tirer, Raja Giryes
论文：https://arxiv.org/abs/1912.00157

Residual Feature Aggregation Network for Image Super-Resolution

论文：http://openaccess.thecvf.com/content_CVPR_2020/papers/Liu_Residual_Feature_Aggregation_Network_for_Image_Super-Resolution_CVPR_2020_paper.pdf
代码：
解析：超越RCAN，图像超分又一峰：RFANet
备注：超越RCAN，图像超分又一峰：RFANet

Deep Unfolding Network for Image Super-Resolution

论文：https://arxiv.org/abs/2003.10428
代码：https://github.com/cszn/USRNet
解析：CVPR2020：USRNet
备注：USRNet

Image Super-Resolution With Cross-Scale Non-Local Attention and Exhaustive Self-Exemplars Mining

论文：https://arxiv.org/abs/2006.01424
代码：https://github.com/SHI-Labs/Cross-Scale-Non-Local-Attention

Learning Texture Transformer Network for Image Super-Resolution

论文：https://arxiv.org/abs/2006.04139
代码：GitHub - researchmm/TTSR: [CVPR'20] TTSR: Learning Texture Transformer Network for Image Super-Resolution
备注：注意力机制

Robust Reference-Based Super-Resolution With Similarity-Aware Deformable Convolution

论文：CVPR 2020 Open Access Repository

Structure-Preserving Super Resolution with Gradient Guidance

论文：https://arxiv.org/abs/2003.13063
代码：https://github.com/Maclory/Deep-Iterative-Collaboration
解析：CVPR2020丨SPSR：基于梯度指导的结构保留超分辨率方法
备注：SPSR

Unified Dynamic Convolutional Network for Super-Resolution With Variational Degradations

论文：https://arxiv.org/abs/2004.06965
代码：
解析：UDVD：适用于可变降质类型的通用图像超分，附参考代码
备注：UDVD

Nested Scale-Editing for Conditional Image Synthesis

论文：http://arxiv.org/abs/2006.02038
备注：解耦表征、多模图像转换、超分、修复

MSG-GAN: Multi-Scale Gradients for Generative Adversarial Networks

论文：https://arxiv.org/abs/1903.06048v3
代码：https://github.com/akanimax/msg-stylegan-tf
解析：CVPR2020之MSG-GAN：简单有效的SOTA
备注：NTIRE2020极限超分亚军方案CIPLab；Workshops

Guided Frequency Separation Network for Real-World Super-Resolution

论文：http://openaccess.thecvf.com/content_CVPRW_2020/papers/w31/Zhou_Guided_Frequency_Separation_Network_for_Real-World_Super-Resolution_CVPRW_2020_paper.pdf
代码：
解析：CVPR2020 ｜高低频分离超分方案
备注：NTIRE2020极限超分前五方案；Workshops

视频超分辨率

TDAN: Temporally-Deformable Alignment Network for Video Super-Resolution

论文：https://arxiv.org/abs/1812.02898
代码：GitHub - YapengTian/TDAN-VSR-CVPR-2020: TDAN: Temporally-Deformable Alignment Network for Video Super-Resolution, CVPR 2020
Demo Video：https://www.youtube.com/watch?v=eZExENE50I0
备注：首次将形变卷积用到视频超分领域；TDAN

Zooming Slow-Mo: Fast and Accurate One-Stage Space-Time Video Super-Resolution

论文：https://arxiv.org/abs/2002.11616
代码：GitHub - Mukosame/Zooming-Slow-Mo-CVPR-2020: Fast and Accurate One-Stage Space-Time Video Super-Resolution (accepted in CVPR 2020)
解析：慢镜头变焦：视频超分辨率：CVPR2020论文解析

Video Super-Resolution With Temporal Group Attention

论文：http://openaccess.thecvf.com/content_CVPR_2020/papers/Isobe_Video_Super-Resolution_With_Temporal_Group_Attention_CVPR_2020_paper.pdf

Space-Time-Aware Multi-Resolution Video Enhancement

主页：Space-Time-Aware Multi-Resolution Video Enhancement
论文：http://arxiv.org/abs/2003.13170
代码：https://github.com/alterzero/STARnet

人脸超分辨率

Learning to Have an Ear for Face Super-Resolution

论文：https://arxiv.org/abs/1909.12780

Deep Face Super-Resolution With Iterative Collaboration Between Attentive Recovery and Landmark Estimation

论文：https://arxiv.org/abs/1812.02898
代码：https://github.com/YapengTian/TDAN-VSR-CVPR-2020

深度图超分辨率

Channel Attention Based Iterative Residual Learning for Depth Map Super-Resolution

论文：https://arxiv.org/abs/2006.01469

光场图像超分辨率

Light Field Spatial Super-Resolution via Deep Combinatorial Geometry Embedding and Structural Consistency Regularization

论文：https://arxiv.org/abs/2004.02215
代码：GitHub - jingjin25/LFSSR-ATO: Repository for "Light Field Spatial Super-resolution via Deep Combinatorial Geometry Embedding and Structural Consistency Regularization" , CVPR 2020

高光谱图像超分辨率

Unsupervised Adaptation Learning for Hyperspectral Imagery Super-Resolution

论文：http://openaccess.thecvf.com/content_CVPR_2020/papers/Zhang_Unsupervised_Adaptation_Learning_for_Hyperspectral_Imagery_Super-Resolution_CVPR_2020_paper.pdf
代码：https://github.com/JiangtaoNie/UAL

零样本超分辨率

Meta-Transfer Learning for Zero-Shot Super-Resolution

论文：https://arxiv.org/abs/2002.12213
代码：https://github.com/JWSoh/MZSR

用于超分辨率的数据增广

Rethinking Data Augmentation for Image Super-resolution: A Comprehensive Analysis and a New Strategy

论文：CVPR 2020 Open Access Repository
代码：GitHub - clovaai/cutblur: Rethinking Data Augmentation for Image Super-resolution (CVPR 2020)

超分辨率用于语义分割

Dual Super-Resolution Learning for Semantic Segmentation

论文：CVPR 2020 Open Access Repository
代码：https://github.com/wanglixilinx/DSRL

2.图像恢复

Learning Invariant Representation for Unsupervised Image Restoration

论文：https://arxiv.org/pdf/2003.12769.pdf
代码：https://github.com/Wenchao-Du/LIR-for-Unsupervised-IR

Contextual Residual Aggregation for Ultra High-Resolution Image Inpainting

论文：https://arxiv.org/abs/2005.09704
备注：超高分辨率图像修复、注意力机制

UCTGAN: Diverse Image Inpainting based on Unsupervised Cross-Space

论文：http://openaccess.thecvf.com/content_CVPR_2020/papers/Zhao_UCTGAN_Diverse_Image_Inpainting_Based_on_Unsupervised_Cross-Space_Translation_CVPR_2020_paper.pdf
备注：图像修复、注意力机制

Attentive Normalization for Conditional Image Generation

论文：https://arxiv.org/abs/2004.03828
备注：注意力机制、类条件图像生成、图像修复

3.去雨

Deep Adversarial Decomposition: A Unified Framework for Separating Superimposed Images

论文：CVPR 2020 Open Access Repository

Multi-Scale Progressive Fusion Network for Single Image Deraining

论文：https://arxiv.org/abs/2003.10985

代码：https://github.com/kuihua/MSPFN

4.去雾

Domain Adaptation for Image Dehazing

论文：https://arxiv.org/abs/2005.04668

Multi-Scale Boosted Dehazing Network with Dense Feature Fusion

论文：https://arxiv.org/abs/2004.13388
代码：GitHub - BookerDeWitt/MSBDN-DFF: The source code of CVPR 2020 paper "Multi-Scale Boosted Dehazing Network with Dense Feature Fusion"

5.去模糊

视频去模糊

Cascaded Deep Video Deblurring Using Temporal Sharpness Prior

主页：CDVD-TSP
论文：https://arxiv.org/abs/2004.02501
代码：GitHub - csbhr/CDVD-TSP: The repository is an official implementation of our CVPR2020 paper : Cascaded Deep Video Deblurring Using Temporal Sharpness Prior

6.去噪

A Physics-based Noise Formation Model for Extreme Low-light Raw Denoising

论文：https://arxiv.org/abs/2003.12751

代码：GitHub - Vandermode/ELD: A Physics-based Noise Formation Model for Extreme Low-light Raw Denoising (CVPR 2020 Oral & TPAMI 2021)

CycleISP: Real Image Restoration via Improved Data Synthesis

论文：https://arxiv.org/abs/2003.07761

代码：GitHub - swz30/CycleISP: Official repository for "CycleISP: Real Image Restoration via Improved Data Synthesis" (CVPR 2020--Oral)

未完待续~

参考

[1] 杜克大学提出 AI 算法，拯救渣画质马赛克秒变高清
 [2] CVPR 2020 论文大盘点-超分辨率篇
 [3] CVPR2020丨SPSR：基于梯度指导的结构保留超分辨率方法
 [4] CVPR2020：USRNet
[5] UDVD：适用于可变降质类型的通用图像超分，附参考代码
 [6] NTIRE2020冠军方案RFB-ESRGAN：带感受野模块的超分网络
 [7] 超越RCAN，图像超分又一峰：RFANet
[8] #每日五分钟一读#Image Super-Resolution
[9] CVPR 2020 | 几篇GAN在low-level vision中的应用论文
 [10] 超100篇！CVPR 2020最全GAN论文梳理汇总！
[11] CVPR2020之MSG-GAN：简单有效的SOTA
[12] CVPR2020-Code
[13] 慢镜头变焦：视频超分辨率：CVPR2020论文解析
 [14] CVPR2020 ｜高低频分离超分方案

以上是关于CVPR2020｜图像重建(超分辨率，图像恢复，去雨，去雾，去模糊，去噪等)相关论文汇总（附论文链接/开源代码/解析）的主要内容，如果未能解决你的问题，请参考以下文章

图像重建基于matlab卷积神经网络的图像超分辨率重建含Matlab源码 1816期

[转]图像超分辨率重建简介

JPEG压缩图像超分辨率重建算法

图像重建基于matlab正则化图像超分辨重建含Matlab源码 1882期

图像重建基于matlab L1范数自适应双边总变分超分辨率图像序列重建含Matlab源码 2209期