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[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.

Contents

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Metrics dispute

Suggestion in SR: CVPR2018 "The Perception-Distortion Tradeoff"

Latest survey

Upscale method

Unsupervised Super-Resolution Method

  1. "Zero-Shot" Super-Resolution using Deep Internal Learning, CVPR2018
  2. Unsupervised image super-resolution using cycle-in-cycle generative adversarial networks, CVPRW2018
  3. Adversarial training with cycle consistency for unsupervised super-resolution in endomicroscopy, Medical image analysis 2019
  4. Self-Supervised Fine-tuning for Image Enhancement of Super-Resolution Deep Neural Networks, arXiv2019
  5. Unsupervised Learning for Real-World Super-Resolution, arXiv2019
  6. Unsupervised Single-Image Super-Resolution with Multi-Gram Loss, MDPI2019

Real-Word Image Super-Resolution

  1. Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data, ICCVW2021
    codes

  2. Toward Bridging the Simulated-to-Real Gap: Benchmarking Super-Reslution on Real Data, TPAMI2019

  3. Toward Real-World Single Image Super-Resolution: A New Benchmark and A New Model, ICCV2019

  4. Camera Lens Super-Resolution, CVPR2019

  5. Zoom to Learn, Learn to Zoom, CVPR2019

  1. Finding Discriminative Filters for Specific Degradations in Blind Super-Resolution, NeurIPS2021
    codes

  2. Blind Super-Resolution with Iterative Kernel Corrections, CVPR2019

  3. Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels, CVPR2019

  4. Blind Super-Resolution Kernel Estimation using an Internal-GAN, NeurIPS2019

  5. Kernel Modeling Super-Resolution on Real Low-Resolution Images, ICCV2019

  6. Unsupervised Degradation Representation Learning for Blind Super-Resolution, CVPR2021
    pytorch-codes

  7. Flow-based Kernel Prior with Application to Blind Super-Resolution, CVPR2021
    pytorch-codes

Stereo Image Super-Resolution

  1. Enhancing the Spatial Resolution of Stereo Images using a Parallax Prior, CVPR2018
<!-- StereoSR,one left LR and one right LR as inputs, but 64 copies of right LR before to luminance net, first learn luminance then to map to RGB by chrominance net, YCbCr to RGB -->
  1. Learning Parallax Attention for Stereo Image Super-Resolution, CVPR2019
<!-- PASSRnet, proposed PAM (parallax attention modual), new Flicker1024 datasets, extend to another: Parallax-based Spatial and Channel Attention Stereo SR network paper by it -->
  1. Stereoscopic Image Super‑Resolution with Stereo Consistent Feature, AAAI2020 oral
<!-- SPAMnet, Self and Parallax Attention Mechanism (SPAM), new loss: Stereo-consistency Loss for stereo consistence, disparity map-->
  1. A Stereo Attention Module for Stereo Image Super-Resolution, SPL2020
<!-- SAM (Stereo attention module), SAM can inset to any SR model, fine-tune after inserting SAM -->

Image Super-Resolution

Sorted by year and the format is: abbreviation, paper title, publicaiton, [highlights], related source code.

In 2021
In 2020
In 2019
In 2018
In 2017
In 2016
In 2014

Video Super-Resolution

Sorted by year and the format is: abbreviation, paper title, publicaiton, [highlights], related source code.

In 2022

In 2021
In 2020
In 2019
In 2018
In 2017
In 2015

Library

Related Research institutions