Latest development of ISR/VSR. Papers and related resources, mainly state-of-the-art and novel works in ICCV, ECCV and CVPR about image super-resolution and video super-resolution.
[Updating...] Papers and related resources, mainly state-of-the-art and novel works in ICCV, ECCV and CVPR about image super-resolution and video super-resolution.
Suggestion in SR: CVPR2018 "The Perception-Distortion Tradeoff"
Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data, ICCVW2021
codes
Toward Bridging the Simulated-to-Real Gap: Benchmarking Super-Reslution on Real Data, TPAMI2019
Toward Real-World Single Image Super-Resolution: A New Benchmark and A New Model, ICCV2019
Camera Lens Super-Resolution, CVPR2019
Zoom to Learn, Learn to Zoom, CVPR2019
Finding Discriminative Filters for Specific Degradations in Blind Super-Resolution, NeurIPS2021
codes
Blind Super-Resolution with Iterative Kernel Corrections, CVPR2019
Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels, CVPR2019
Blind Super-Resolution Kernel Estimation using an Internal-GAN, NeurIPS2019
Kernel Modeling Super-Resolution on Real Low-Resolution Images, ICCV2019
Unsupervised Degradation Representation Learning for Blind Super-Resolution, CVPR2021
pytorch-codes
Flow-based Kernel Prior with Application to Blind Super-Resolution, CVPR2021
pytorch-codes
Sorted by year and the format is: abbreviation, paper title, publicaiton, [highlights], related source code.
A Practical Contrastive Learning Framework for Single Image Super-Resolution, arXiv2021, [contrastive, discriminator, data-augment, task-generalizable embedding]
GLEAN, GLEAN: Generative Latent Bank for Large-Factor Image Super-Resolution, CVPR2021 Oral, [encoder-bank-decoder, StyleGAN as generative latent bank]
pytorch-codes
ClassSR, ClassSR: A General Framework to Accelerate Super-Resolution Networks by Data Characteristic, CVPR2021
pytorch-codes
LIIF, Learning Continuous Image Representation with Local Implicit Image Function, CVPR2021
pytorch-codes
AdderSR, AdderSR: Towards Energy Efficient Image Super-Resolution, CVPR2021
IPT, Pre-Trained Image Processing Transformer, arXiv2020, [low-level transformer]
waiting
IGNN, Cross-Scale Internal Graph Neural Network for Image Super-Resolution, NeurIPS2020, [graph related, patch match]
codes
SRFlow, SRFlow: Learning the Super-Resolution Space with Normalizing Flow, ECCV2020
codes-prepare
PISR, Learning with Privileged Information for Efficient Image Super-Resolution, ECCV2020, [use encoder and decoder in teacher, distillation, estimator module]
pytorch-codes
Coarse-to-fine cnn for image super-resolution, IEEE TMM2020
pytorch-codes
Lightweight Image Super-Resolution with Enhanced CNN, arXiv2020, Elsevier
pytorch-codes
Unpaired Image Super-Resolution using Pseudo-Supervision, CVPR2020
Single-Image HDR Reconstruction by Learning to Reverse the Camera Pipeline, CVPR2020
tensorflow-codes
Invertible Image Rescaling, ECCV2020, [another method to get more information in the sacaling phase, invertible NN, flow-based, wavelet transform]
codes
IGNN, Cross-Scale Internal Graph Neural Network for Image Super-Resolution, arXiv2020, [first use the graph neural network, graph construction and patch aggreagation module, find the k similar neighbor patch]
codes
TTSR, Learning Texture Transformer Network for Image Super-Resolution, CVPR2020, [proposed a transformer-based model to do SR, texture transformer]
[codes-wait]
CutBlur, Rethinking Data Augmentation for Image Super-resolution: A Comprehensive Analysis and a New Strategy, CVPR2020, [new data augmentation method called CutBlur, it not only can tackle SR but other low-level tack like denoising and artifact ramoval, cut-and-paste based on patch, let model to know where to SR and how to SR]
pytorch-codes
SPSR, Structure-Preserving Super Resolution with Gradient Guidance, CVPR2020, [Gradient guidance to perserve the information, gradient loss, address the geometric distort]
pytorch-codes
UDVD, Unified Dynamic Convolutional Network for Super-Resolution with Variational Degradations, CVPR2020, [try to use one model to address several degreadation, Feature Extraction Network(FRN), Refinement Network(RN), Dynamic Block(DB), dynamic conv by a dynamic kernels some like sub-pixel operation]
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SRFBN, Feedback Network for Image Super-Resolution, CVPR2019, [feedback and a lot of comparation]
pytorch-codes
zoom-learn-zoom, Zoom to Learn, Learn to Zoom, CVPR2019, [SR-RAW dataset and CoBi loss, real-word, new direction for SR-RAW datasets and new CoBi loss function for alignment]
tensorflow-codes
Camera, Camera Lens Super-Resolution, CVPR2019, [real-word, Create City100 Dataset for real-word application]
tensorflow-codes
RealSR, Toward Real-World Single Image Super-Resolution: A New Benchmark and A New Model, ICCV2019, [RealSR dataset, real-word, LP-KPN, New RealSR datasets more flexible and convenient to use]
caffe-codes
Simulated-to-Real Gap, Toward Bridging the Simulated-to-Real Gap: Benchmarking Super-Reslution on Real Data, TPAMI2019, [hardware binning, real-word, maybe the method older for it's journal]
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RankSRGAN, RankSRGAN: Generative Adversarial Networks with Ranker for Image Super- Resolution, CVPR2019, [focus on perceptual quality, and new method to use perceptual metrics named Ranker]
pytorch-codes
IMDN, Lightweight Image Super-Resolution with Information Multi-distillation Network, ACM MM2019
pytorch-codes
WDSR, Wide Activation for Efficient and Accurate Image Super-Resolution, arXiv2018, [widen feature map and WN, weight normalization]
pytorch-codes
SRMD, Learning a Single Convolutional Super-Resolution Network for Multiple Degradations, CVPR2018, Degraded Fuzzy Kernel and Noise Level
matlab-codes
RDN, Residual Dense Network for Image Super-Resolution, CVPR2018 Spotlight, [local and global Residual, bicubic downsampling, gaussian kernel feature fusing]
official-codes
DBPN, Deep Back-Projection Networks For Super-Resolution, CVPR2018, [repeat down and up sample a back mechanism, Back-Projection]
pytorch-codes
ZSSR, "Zero-Shot" Super-Resolution using Deep Internal Learning, CVPR2018, [re-sample train test, internally train, zero-shot]
pytorch-codes
SFTGAN, Recovering Realistic Texture in Image Super-resolution by Deep Spatial Feature Transform, CVPR2018, [semantic probability, semantic SFT]
pytorch-codes
EUSR, Deep Residual Network with Enhanced Upscaling Module for Super-Resolution, CVPR2018, [enhanced upscaling module (EUM), change EDSR to EUSR by adding EUM]
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CARN, Fast, Accurate, and Lightweight Super-Resolution with Cascading Residual Network, ECCV2018, [fast, cascading block]
pytorch-codes
GAN_degradation, To learn image super-resolution, use a GAN to learn how to do image degradation first, ECCV2018, [mainly face test, use GAN to prodecu LR near to nature,]
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RCAN, Image Super-Resolution Using Very Deep Residual Channel Attention Networks, ECCV2018, [Deep, Residual, Channel Attention, very deep residual block with channel attention using several skip connection and channel weight]
pytorch-codes
EPSR, Analyzing Perception-Distortion Tradeoff using Enhanced Perceptual Super-resolution Network, ECCV2018, [has a new metrics idea]
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DRRN, Image Super-Resolution via Deep Recursive Residual Network, CVPR2017, [residual network, combine ResNet and recursive]
caffe-codes
LapSRN, Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution, CVPR2017, [Pyramid network new loss to constrain]
matconvnet-codes | pytorch | tensorflow
SRDenseNet, Image Super-Resolution Using Dense Skip Connections, ICCV2017, [add dense block to model]
pytorch-codes
SRGAN, Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network, CVPR2017, [first proposed GAN]
tensorflow | tensorflow |
torch |
caffe |
tensorflow |
keras |
pytorch
EDSR, Enhanced Deep Residual Networks for Single Image Super-Resolution, CVPR2017, [remove BN]
torch | tensorflow |
pytorch
FSRCNN, Accelerating the Super-Resolution Convolutional Neural Network, ECCV2016, [deconvolution fine-tuninig last deconv, Develop SRCNN, add deconv, input image don't need to upsample by bicubic and fine-tune accelerate]
official-matlab-caffe-codes
ESPCN, Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network, CVPR2016, [sub-pixel Tanh instead Relu Real time, A new way to upsamping: sub-pixel]
tensorflow-codes | pytorch-codes | caffe-codes
VDSR, Accurate Image Super-Resolution Using Very Deep Convolutional Networks, CVPR2016, [residual network, deep, Add residual, padding 0 every layer, scale mixture training]
project | caffe | tensorflow | pytorch
DRCN, Deeply-Recursive Convolutional Network for Image Super-Resolution, CVPR2016, [Recursive Neural Network, Learn RNN to add recursive and skip input image is interpolation image]
project | tensorflow
RED, Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections, NIPS2016, [Encoder-decoder and skip]
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Sorted by year and the format is: abbreviation, paper title, publicaiton, [highlights], related source code.
DAP, Fast Online Video Super-Resolution with Deformable Attention Pyramid, arXiv2022, [deformable attention pyramid, fast]
Self-Blind-VSR,Self-Supervised Deep Blind Video Super-Resolution, arXiv2022, [for real bind vsr without HR, auxiliary paired data]
project
RealBasicVSR, Investigating Tradeoffs in Real-World Video Super-Resolution, arXiv2021, [cleaning module, new videoLQ dataset, real VSR]
Pytorch-codes
VSR-transformer, Video Super-Resolution Transformer, arXiv2021, [transformer in VSR]
Pytorch-codes
GLEAN, Glean: Generative latent bank for large-factor image super-resolution, CVPR2021 oral, [Generative LatEnt bANk(GLEAN), encoder-bank-decoder architecture with multiresolution skip connections]
Pytorch-codes
BasicVSR, BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond, CVPR2021, [BasicVSR architecture, research on essential component]
Pytorch-codes
RBPN, Recurrent Back-Projection Network for Video Super-Resolution, CVPR2019, [recurrent encoder-decoder module]
Pytorch-codes
EDVR, EDVR: Video Restoration with Enhanced Deformable Convolutional Networks, CVPR2019, [PCD:Pyramid, Cascading and Deformable (PCD) alignment module, TSA:Temporal and Spatial Attention fusion module, proposed two specify modules: PCD and TSA. PCD is for alignment and STA is for fusion. With deformable convolution, self-ensemble and two-stage redfine, it wins all four tracks in the NTIRE19 Challenges for Video]
Pytorch-codes
FRVSR, Frame-Recurrent Video Super-Resolution, CVPR2018, [use a recurrent approach that passes the previously estimated HR frame as an input for the following iteration. Model includes Fnet and SRNet, Flow estimation, Upscaling flow, Warping previous output, Mapping to LR space, Super-Resolution Warp]
official-codes
DUF, Deep Video Super-Resolution Network Using Dynamic Upsampling Filters Without Explicit Motion Compensation, CVPR2018, [Dynamic upsampling filter, Residual Learning, propose a novel end-to-end deep neural network that generates dynamic upsampling filters and a residual image, which are computed depending on the local spatio-temporal neighborhood of each pixel to avoid explicit motion compensation. The model includes filter generation network and residual generation network]
tensorflow-codes |
tensorflow
VESPCN, Real-Time Video Super-Resolution with Spatio-Temporal Networks and Motion Compensation, CVPR2017, [sub-pixel for video compensation transformer, compensation transformer: compare early fusion, slow fusion and 3D conv]
pytorch | tensorflow
SPMC, Detail-revealing Deep Video Super-resolution, ICCV2017, [SPMC: Subpixel Motion Compensation layer, show that proper frame alignment and motion compensation is crucial for achieving high quality results, It includes motion estimate, SPMC layer and Detail Fusion Net]
tensorflow-codes
A collection of state-of-the-art video or single-image super-resolution architectures, reimplemented in tensorflow. which has most great papers/models about ISR and VSR. Include some useful tools: some models with pre-trained weights, link of datasets, VSR package which offers a training and data processing framework based on TF or pytorch.
MMEditing, MMEditing is an open source image and video editing toolbox based on PyTorch. It is a part of the OpenMMLab project