A collection of state-of-the-art video or single-image super-resolution architectures, reimplemented in tensorflow. https://github.com/LoSealL/VideoSuperResolution
超分辨率开发工具集 https://github.com/xinntao/BasicSR
Recovering Realistic Texture in Image Super-resolution by Deep Spatial Feature Transform https://github.com/xinntao/SFTGAN
FALSR：快速、准确、轻量级超分辨率模型 https://github.com/falsr/FALSR
Tensorflow implementation of the paper Image Super-Resolution by Neural Texture Transfer accepted in CVPR 2019. https://github.com/ZZUTK/SRNTT
Official code (Tensorflow) for paper "Fast and Efficient Image Quality Enhancement via Desubpixel Convolutional Neural Networks" https://github.com/thangvubk/FEQE
Pytorch code for our paper "Feedback Network for Image Super-Resolution" (CVPR2019) https://github.com/Paper99/SRFBN_CVPR19
Repository for Detail-revealing Deep Video Super-resolution https://arxiv.org/abs/1704.02738
https://github.com/jiangsutx/SPMC_VideoSR
Unsupervised Real Image Super-Resolution via Generative Variational AutoEncoder https://github.com/Holmes-Alan/dSRVAE
Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels (CVPR, 2019) (PyTorch) https://github.com/cszn/DPSR
(PyTorch)超分辨率模型、算法集锦 https://github.com/icpm/super-resolution
Official implementation of Meta-SR: A Magnification-Arbitrary Network for Super-Resolution(CVPR2019)(PyTorch) https://github.com/XuecaiHu/Meta-SR-Pytorch
Meta-Transfer Learning for Zero-Shot Super-Resolution (Accepted for CVPR 2020) https://github.com/JWSoh/MZSR
Residual Dense Network for Image Super-Resolution https://github.com/yulunzhang/RDN
多帧超分辨率(MFSR)网络HighRes-net https://github.com/ElementAI/HighRes-net
Lossless Image Compression through Super-Resolution https://github.com/caoscott/SReC
超分辨率相关资源大列表 https://github.com/ptkin/Awesome-Super-Resolution
PyTorch code for our paper "Lightweight Image Super-Resolution with Adaptive Weighted Learning Network" https://github.com/ChaofWang/AWSRN
无需GAN的深度学习超分辨率 https://towardsdatascience.com/deep-learning-based-super-resolution-without-using-a-gan-11c9bb5b6cd5
(MXNet)预训练超分辨率模型集锦 https://github.com/WolframRhodium/Super-Resolution-Zoo
A TensorFlow implementation of CVPR 2018 paper "Residual Dense Network for Image Super-Resolution". https://github.com/hengchuan/RDN-TensorFlow
TDAN: Temporally Deformable Alignment Network for Video Super-Resolution https://github.com/YapengTian/TDAN_VSR
Second-order Attention Network for Single Image Super-resolution (CVPR-2019) https://github.com/daitao/SAN
MMSR：基于PyTorch的图像/视频超分辨率工具箱 https://github.com/open-mmlab/mmsr
EDSR/WDSR/SRGAN单张图片超分辨率Tensorflow 2.0参考实现 https://github.com/krasserm/super-resolution
基于waifu2x/Anime4K的视频超分辨率工具 https://github.com/k4yt3x/video2x
【Fast-SRGAN 图像超分辨率】’Fast-SRGAN - A Single Image Super Resolution GAN that uses a mobile net architecture as a generator.' https://github.com/HasnainRaz/Fast-SRGAN
Progressive Perception-Oriented Network for Single Image Super-Resolution https://github.com/Zheng222/PPON
Fast, Accurate and Lightweight Super-Resolution models https://github.com/xiaomi-automl/FALSR
《Zooming Slow-Mo: Fast and Accurate One-Stage Space-Time Video Super-Resolution》 https://github.com/Mukosame/Zooming-Slow-Mo-CVPR-2020
Official Implementation for Kernel Modeling Super-Resolution on Real Low-Resolution Images https://github.com/IVRL/Kernel-Modeling-Super-Resolution
Progressive Fusion Video Super-Resolution Network via Exploiting Non-Local Spatio-Temporal Correlations https://github.com/psychopa4/PFNL
Official Pytorch Implementation of Progressive Face Super-Resolution (BMVC 2019 Accepted) https://github.com/DeokyunKim/Progressive-Face-Super-Resolution
Toward Real-World Single Image Super-Resolution: A New Benchmark and A New Model (ICCV 2019) https://github.com/csjcai/RealSR
ICCV 2019 (oral) RankSRGAN: Generative Adversarial Networks with Ranker for Image Super-Resolution. https://github.com/WenlongZhang0724/RankSRGAN
Lightweight Image Super-Resolution with Information Multi-distillation Network https://github.com/Zheng222/IMDN
Camera Lens Super-Resolution in CVPR 2019 https://github.com/ngchc/CameraSR
IKC: Blind Super-Resolution With Iterative Kernel Correction https://github.com/yuanjunchai/IKC
《Deep Video Super-Resolution using HR Optical Flow Estimation》 https://www.arxiv-vanity.com/papers/2001.02129/
《Simultaneous Enhancement and Super-Resolution of Underwater Imagery for Improved Visual Perception》 https://github.com/xahidbuffon/Deep-SESR
Waifu2x-Extension-GUI：基于Waifu2x, SRMD, Anime4K的图片/Gif动画/视频超分辨率放大工具 https://github.com/AaronFeng753/Waifu2x-Extension-GUI
【用机器学习和智能实时算法实现动画分辨率提升】 https://medium.com/crunchyroll/scaling-up-anime-with-machine-learning-and-smart-real-time-algorithms-2fb706ec56c0
Image Super-Resolution with Cross-Scale Non-Local Attention and Exhaustive Self-Exemplars Mining https://github.com/SHI-Labs/Cross-Scale-Non-Local-Attention
【深度学习超分辨率工具/模型集】 https://github.com/idearibosome/srzoo
Super-resolution Variational Auto-Encoders https://github.com/ioangatop/srVAE
【图像/视频超分辨率最新进展】 https://github.com/HymEric/latest-development-of-ISR-VSR
【单图片超分辨率Keras实现库(EDSR, SRGAN, SRFeat, RCAN, ESRGAN, ERCA)】 https://github.com/hieubkset/Keras-Image-Super-Resolution
Encoder-Decoder Residual Network for Real Super-Resolution https://github.com/yyknight/NTIRE2019_EDRN
Deep Unfolding Network for Image Super-Resolution (CVPR, 2020) https://github.com/cszn/USRNet
Rethinking Data Augmentation for Image Super-resolution (CVPR 2020) https://github.com/clovaai/cutblur
Closed-loop Matters: Dual Regression Networks for Single Image Super-Resolution https://github.com/guoyongcs/DRN
TDAN: Temporally-Deformable Alignment Network for Video Super-Resolution, CVPR 2020 https://github.com/YapengTian/TDAN-VSR-CVPR-2020
Pytorch implementation of Deep Face Super-Resolution with Iterative Collaboration between Attentive Recovery and Landmark Estimation (CVPR 2020) https://github.com/Maclory/Deep-Iterative-Collaboration
PyTorch Implementation of "Lossless Image Compression through Super-Resolution" https://github.com/caoscott/SReC
Deep Adaptive Inference Networks for Single Image Super-Resolution https://github.com/csmliu/AdaDSR
Pytorch implementation of Structure-Preserving Super Resolution with Gradient Guidance (CVPR 2020) https://github.com/Maclory/SPSR
Stochastic Frequency Masking to Improve Super-Resolution and Denoising Networks https://github.com/sfm-sr-denoising/sfm
HNAS: Hierarchical Neural Architecture Search for Single Image Super-Resolution https://github.com/guoyongcs/HNAS-SR
PyTorch code for our paper "Image Super-Resolution with Cross-Scale Non-Local Attention and Exhaustive Self-Exemplars Mining" https://github.com/SHI-Labs/Cross-Scale-Non-Local-Attention
Official PyTorch implementation of the paper Learning Texture Transformer Network for Image Super-Resolution accepted in CVPR 2020. https://github.com/researchmm/TTSR
[ICCVW 2019] PyTorch implementation of DSGAN and ESRGAN-FS from the paper "Frequency Separation for Real-World Super-Resolution". This code was the winning solution of the AIM challenge on Real-World Super-Resolution at ICCV 2019 https://github.com/ManuelFritsche/real-world-sr
Correction Filter for Single Image Super-Resolution: Robustifying Off-the-Shelf Deep Super-Resolvers https://github.com/shadyabh/Correction-Filter
(ECCV 2020) Stochastic Frequency Masking to Improve Super-Resolution and Denoising Networks https://github.com/majedelhelou/SFM
An official implementation of "Learning with Privileged Information for Efficient Image Super-Resolution" (ECCV2020) in PyTorch. https://github.com/cvlab-yonsei/PISR
Natural and Realistic Single Image Super-Resolution with Explicit Natural Manifold Discrimination (CVPR, 2019) https://github.com/JWSoh/NatSR
CASSP 2020 - ESRGAN+ : Further Improving Enhanced Super-Resolution Generative Adversarial Network - Pytorch implementation https://github.com/ncarraz/ESRGANplus
Repository for "A Stereo Attention Module for Stereo Image Super-Resolution ", https://github.com/XinyiYing/SAM
Upscale an image by a factor of 4, while generating photo-realistic details. https://github.com/IBM/MAX-Image-Resolution-Enhancer
TDAN: Temporally-Deformable Alignment Network for Video Super-Resolution, CVPR 2020 https://github.com/YapengTian/TDAN-VSR-CVPR-2020
立体视觉超分辨率算法/数据集大列表 https://github.com/YingqianWang/Awesome-Stereo-Image-SR
Keras实例教程：高效子像素CNN图像超分辨率 https://keras.io/examples/vision/super_resolution_sub_pixel/
SynthSR：超分辨率/图像合成联合框架 https://github.com/BBillot/SynthSR
SplitSR: An End-to-End Approach to Super-Resolution on Mobile Devices https://arxiv.org/abs/2101.07996
Ojoy：基于UpscalerJS & TensorFlow.js完全在客户端实现运算的图片超分辨率应用 https://ojoy.netlify.app/
Component Divide-and-Conquer for Real-World Image Super-Resolution(CDC) https://github.com/xiezw5/Component-Divide-and-Conquer-for-Real-World-Image-Super-Resolution
Densely Residual Laplacian Network (DRLN) https://github.com/saeed-anwar/DRLN
Real-World Super-Resolution via Kernel Estimation and Noise Injection https://github.com/Tencent/Real-SR
TDAN: Temporally-Deformable Alignment Network for Video Super-Resolution https://github.com/YapengTian/TDAN-VSR-CVPR-2020
ImageStackAlignator：Google手机端多帧超分辨率算法实现(C sharp) https://github.com/kunzmi/ImageStackAlignator
VAE with RealNVP prior and Super-Resolution VAE in PyTorch. Code release for https://arxiv.org/abs/2006.05218 https://github.com/ioangatop/srVAE
Implementation of Google's Handheld Multi-Frame Super-Resolution algorithm (from Pixel 3 and Pixel 4 camera) https://github.com/kunzmi/ImageStackAlignator
Efficient Image Super-Resolution Using Pixel Attention, in ECCV Workshop, 2020. https://github.com/zhaohengyuan1/PAN
Training and Testing codes for our paper "Real-world Image Super-resolution via Domain-distance Aware Training" https://github.com/ShuhangGu/DASR
Residual Feature Distillation Network for Lightweight Image Super-Resolution https://github.com/njulj/RFDN
Recurrent Residual Network for Video Super-resolution (RRN) https://github.com/junpan19/RRN
A PyTorch implementation of SRNTT[1], which is a novel Reference-based Super-Resolution method proposed in CVPR 2019. https://github.com/S-aiueo32/srntt-pytorch
HyperRIM: Hyper-Resolution Implicit Model https://github.com/niopeng/HyperRIM
DynaVSR: Dynamic Adaptive Blind VideoSuper-Resolution https://github.com/esw0116/DynaVSR
Keras Implementation of the paper Residual Feature Distillation Network for Lightweight Image Super-Resolution https://github.com/tuvovan/RFDNet-ImageSuperResolution
Coarse-to-Fine CNN for Image Super-Resolution (IEEE Transactions on Multimedia,2020) https://github.com/hellloxiaotian/CFSRCNN
MuCAN: Multi-Correspondence Aggregation Network for Video Super-Resolution https://github.com/Jia-Research-Lab/Simple-SR
Explorable Super Resolution https://github.com/YuvalBahat/Explorable-Super-Resolution
code for paper "Video Super-resolution with Temporal Group Attention" https://github.com/junpan19/VSR_TGA
Official repository containing code and other material from the paper "Efficient Video Super-Resolution through Recurrent Latent Space Propagation" https://github.com/dariofuoli/RLSP
Learning Cascaded Convolutional Networks for Blind Single Image Super-Resolution https://github.com/lpj0/CBSR
2020.12 Learning Continuous Image Representation with Local Implicit Image Function https://github.com/yinboc/liif https://arxiv.org/abs/2012.09161
《Deep Generative Adversarial Residual Convolutional Networks for Real-World Super-Resolution》(2020) github.com/RaoUmer/SRResCGAN
《Exploring Sparsity in Image Super-Resolution for Efficient Inference》(CVPR 2021) github.com/LongguangWang/SMSR
《Investigating Loss Functions for Extreme Super-Resolution》(CVPR 2020) github.com/kingsj0405/ciplab-NTIRE-2020
《Exploiting raw images for real-scene super-resolution》(PAMI 2021) github.com/xuxy09/RawSR
SRWarp: Generalized Image Super-Resolution under Arbitrary Transformation github.com/sanghyun-son/srwarp
Temporal Modulation Network for Controllable Space-Time Video Super-Resolution (CVPR 2021) github.com/CS-GangXu/TMNet
《MASA-SR: Matching Acceleration and Spatial Adaptation for Reference-Based Image Super-Resolution》(CVPR 2021) github.com/Jia-Research-Lab/MASA-SR
《Robust Reference-based Super-Resolution via C2-Matching》(CVPR 2021) github.com/yumingj/C2-Matching
《Unsupervised Degradation Representation Learning for Blind Super-Resolution》(CVPR 2021) github.com/LongguangWang/DASR
Fourier Space Losses for Efficient Perceptual Image Super-Resolution https://www.arxiv-vanity.com/papers/2106.00783
Super-Resolution Networks for Pytorch：超分辨率模型PyTorch实现与数据集列表 github.com/Coloquinte/torchSR
UpscalerJS：基于Tensorflow.js的图像4x超分辨率包(JS) github.com/thekevinscott/UpscalerJS
Real-Time Super-Resolution System of 4K-Video Based on Deep Learning github.com/Thmen/EGVSR
基于条件扩散模型的图像超分辨率 iterative-refinement.github.io/
EGVSR-PyTorch：高效通用视频超分辨率 github.com/Thmen/EGVSR
SwinIR: Image Restoration Using Swin Transformer github.com/JingyunLiang/SwinIR
用TensorFlow、Keras和深度学习实现Pixel Shuffle超分辨率 | PyImageSearch https://www.pyimagesearch.com/2021/09/27/pixel-shuffle-super-resolution-with-tensorflow-keras-and-deep-learning/
Real-ESRGAN 是一款图像分辨率修复工具，它可以提升照片、动画图片的分辨率，内置了一个预训练模型，可以提升 4 倍分辨率。虽然是命令行工具，但使用简单，效果也非常不错 https://www.appinn.com/real-esrgan/
github.com/braindotai/Real-Time-Super-Resolution
《Investigating Tradeoffs in Real-World Video Super-Resolution》 github.com/ckkelvinchan/RealBasicVSR
Real-CUGAN:用百万级动漫数据训练的，结构与Waifu2x兼容的通用动漫图像超分辨率模型 github.com/bilibili/ailab/tree/main/Real-CUGAN
traiNNer：基于PyTorch用于图像和视频超分辨率、恢复和图像到图像翻译的深度学习框架 github.com/victorca25/traiNNer
Single Image Super-Resolution Methods: A Survey https://arxiv.org/abs/2202.11763
超分辨率开发工具集 github.com/XPixelGroup/BasicSR
agi-upscale：老游戏像素背景图超分辨率工具 github.com/eviltrout/agi-upscale
QualityScaler：Windows下的图像/视频深度学习超分辨率App ’QualityScaler - Image/video deeplearning upscaler app for Windows - BRSGAN & RealSR_JPEG' by Annunziata Gianluca GitHub: github.com/Djdefrag/QualityScaler
【FidelityFX Super Resolution(FSR)：FidelityFX超分辨率框架】’FidelityFX Super Resolution 2.0.1 (FSR 2.0) - FidelityFX Super Resolution 2' by GPUOpen Effects GitHub: github.com/GPUOpen-Effects/FidelityFX-FSR2
【BasicsR：图像、视频超分复原增强开源库中文解读文档】'BasicSR 中文解读文档' by XPixelGroup GitHub: github.com/XPixelGroup/BasicSR-docs
[CV]《Rethinking Alignment in Video Super-Resolution Transformers》S Shi, J Gu, L Xie, X Wang, Y Yang, C Dong [Tsinghua University & Shanghai AI Laboratory & Chinese Academy of Sciences & Tencent PCG] (2022) https://arxiv.org/abs/2207.08494
[CV]《NeuriCam: Video Super-Resolution and Colorization Using Key Frames》B Veluri, A Saffari, C Pernu, J Smith, M Taylor, S Gollakota [University of Washington] (2022)
https://arxiv.org/abs/2207.12496
【Upscayl：开源的跨平台图像超分辨率工具】’Upscayl - Free and Open Source AI Image Upscaler for Linux, MacOS and Windows' by TGS963 GitHub: github.com/TGS963/upscayl
图像超分辨率模型，修复漫画图像的效果惊艳。通过 AI 技术将低分辨率、模糊的图像修复成高清图像，可用于图像放大和提升质量 https://github.com/xinntao/Real-ESRGAN
[CV]《Hitchhiker's Guide to Super-Resolution: Introduction and Recent Advances》B Moser, F Raue, S Frolov, J Hees, S Palacio, A Dengel [German Research Center for Artificial Intelligence (DFKI)] (2022) https://arxiv.org/abs/2209.13131
【QualityScaler：Windows下的图像/视频深度学习超分辨率App】’QualityScaler - Image/video deeplearning upscaler app for Windows - BRSGAN & RealSR_JPEG' by Annunziata Gianluca GitHub: github.com/Djdefrag/QualityScaler
https://mortenjust.substack.com/p/upscalo
【视频分辨率提升(2x or 4x)】’Upscales Video 2x or 4x using AI' David Lee GitHub: github.com/davlee1972/upscale_video
【SuperImage：安卓图像超分辨率App】’SuperImage - Sharpen your low-resolution pictures with the power of AI upscaling' Lucchetto GitHub: github.com/Lucchetto/SuperImage
基于Real-ESRGAN 微博正文 开发的开源图像修复放大软件，支持Windows、Mac和Linux 项目：🔗github.com/upscayl/upscayl
图片无损修复工具：SwinIR是一个能够帮助你修复和提升图片质量的工具。 你可以把这个工具想象成一个超级强大的”图片医生”。比如说，如果你有一张分辨率不高的图片，你想让它变得更清晰，SwinIR就可以帮你做到这一点。或者，如果你有一张图片，但是图片中有很多噪声（比如颜色不正常的点） SwinIR也可以帮你去除这些噪声。又或者如果你有一张JPEG格式的图片，但是由于压缩过程中产生了一些不自然的效果（我们称之为”压缩伪影”）SwinIR也可以帮你减少这些效果。 这个工具使用了一种叫做Swin Transformer的技术，这种技术在图像处理领域表现得非常出色。已经在Replicate网站上被运行了390万次！ 你可以在Replicate上直接运行这个模型，也可以查看他们的 介绍：replicate.com/jingyunliang/swinir Github：github.com/jingyunliang/swinir 论文：arxiv.org/abs/2108.10257
【Final2x：将图像超分辨率提升到任意大小，旨在提高图像的分辨率和质量，使其更清晰、更详细，支持多个模型】’Final2x - 2^x Image Super-Resolution, Enhance Your Images with Effortless Cross-Platform Super-Resolution at Any Scale’ Tohrusky GitHub: github.com/Tohrusky/Final2