Skip to content

X-Super-Resolution is dedicated to presenting the research efforts of XPixel in the realm of image super-resolution.

License

Notifications You must be signed in to change notification settings

XPixelGroup/X-Super-Resolution

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

6 Commits
 
 
 
 
 
 

Repository files navigation

XPixel Homepage HOT
 

Introduction

X-Super-Resolution is dedicated to presenting the research efforts of XPixel in the realm of image super-resolution. We are thrilled to share research papers and corresponding open-source code crafted by our team.

Super-resolution algorithms aim to reconstruct high-resolution images from low-resolution counterparts, preserving and enhancing important details.

Super-resolution has applications in various domains such as surveillance, medical imaging, satellite imagery, and digital entertainment. It enhances image and video quality, making it invaluable for tasks that require high levels of detail and accuracy.

Table of Contents

Papers

Representative Work:fire::fire::fire:

  • Learning a Deep Convolutional Network for Image Super-Resolution
    Chao Dong, Chen Change Loy, Kaiming He, Xiaoou Tang
    Accepted at ECCV'14
    📜paper 🏠project

    more We propose a deep learning method for single image super-resolution (SR). Our method directly learns an end-to-end mapping between the low/high-resolution images. The mapping is represented as a deep convolutional neural network (CNN) that takes the low-resolution image as the input and outputs the high-resolution one.
  • Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data
    Xintao Wang, Liangbin Xie, Chao Dong, Ying Shan
    Accepted at ICCVW'21
    📜paper 💻code

    more In this work, we extend the powerful ESRGAN to a practical restoration application, which is trained with pure synthetic data. Specifically:
    1. A high-order degradation modeling process is introduced to better simulate complex real-world degradations.
    2. We also consider the common ringing and overshoot artifacts in the synthesis process.
    3. In addition, we employ a U-Net discriminator with spectral normalization to increase discriminator capability and stabilize the training dynamics.

    Extensive comparisons have shown its superior visual performance than prior works on various real datasets.

Blind SR

  • Blind Image Super-Resolution: A Survey and Beyond
    Anran Liu, Yihao Liu, Jinjin Gu, Yu Qiao, Chao Dong
    Accepted at TPAMI'22
    📜paper

  • Crafting Training Degradation Distribution for the Accuracy-Generalization Trade-off in Real-World Super-Resolution
    Ruofan Zhang, Jinjin Gu, Haoyu Chen, Chao Dong, Yulun Zhang, Wenming Yang
    Accepted at ICML'23
    📜paper

  • 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
    Accepted at ICML'23
    📜paper 💻code

  • OSRT: Omnidirectional Image Super-Resolution with Distortion-aware Transformer
    Fanghua Yu, Xintao Wang, Mingdeng Cao, Gen Li, Ying Shan, Chao Dong
    Accepted at CVPR'23
    📜paper 💻code

  • Metric Learning based Interactive Modulation for Real-World Super-Resolution
    Chong Mou, Yanze Wu, Xintao Wang, Chao Dong, Jian Zhang, Ying Shan
    Accepted at ECCV'22
    📜paper 💻code

  • A Closer Look at Blind Super-Resolution: Degradation Models, Baselines, and Performance Upper Bounds
    Wenlong Zhang, Guangyuan Shi, Yihao Liu, Chao Dong, Xiao-Ming Wu
    Accepted at CVPRW'22
    📜paper 💻code

  • GCFSR: a Generative and Controllable Face Super Resolution Method Without Facial and GAN Priors
    Jingwen He, Wu Shi, Kai Chen, Lean Fu, Chao Dong
    Accepted at CVPR'22
    📜paper

  • Reflash Dropout in Image Super-Resolution
    Xiangtao Kong, Xina Liu, Jinjin Gu, Yu Qiao, Chao Dong
    Accepted at CVPR'22
    📜paper

  • Suppressing Model Overfitting for Image Super-Resolution Networks
    Ruicheng Feng, Jinjin Gu, Yu Qiao, Chao Dong
    Accepted at CVPRW'19
    📜paper

  • Blind Super-Resolution With Iterative Kernel Correction
    Jinjin Gu, Hannan Lu, Wangmeng Zuo, Chao Dong
    Accepted at CVPR'19
    📜paper

  • Unsupervised Image Super-Resolution Using Cycle-in-Cycle Generative Adversarial Networks
    Yuan Yuan, Siyuan Liu, Jiawei Zhang, Yongbing Zhang, Chao Dong, Liang Lin
    Accepted at CVPRW'18
    📜paper

Classic SR

  • Activating More Pixels in Image Super-Resolution Transformer
    Xiangyu Chen, Xintao Wang, Jiantao Zhou, Yu Qiao, Chao Dong
    Accepted at CVPR'23
    📜paper 💻code

  • Efficient Image Super-Resolution using Vast-Receptive-Field Attention
    Lin Zhou, Haoming Cai, Jinjin Gu, Zheyuan Li, Yingqi Liu, Xiangyu Chen, Yu Qiao, Chao Dong
    Accepted at ECCVW'22
    📜paper 💻code

  • Blueprint Separable Residual Network for Efficient Image Super-Resolution
    Zheyuan Li, Yingqi Liu, Xiangyu Chen, Haoming Cai, Jinjin Gu, Yu Qiao, Chao Dong
    Accepted at CVPRW'22
    📜paper 💻code

  • RepSR: Training Efficient VGG-style Super-Resolution Networks with Structural Re-Parameterization and Batch Normalization
    Xintao Wang, Chao Dong, Ying Shan
    Accepted at ACM MM'22
    📜paper 💻code

  • ClassSR: A General Framework to Accelerate Super-Resolution Networks by Data Characteristic
    Xiangtao Kong, Hengyuan Zhao, Yu Qiao, Chao Dong
    Accepted at CVPR'21
    📜paper 💻code

  • RankSRGAN: Super Resolution Generative Adversarial Networks with Learning to Rank
    Wenlong Zhang, Yihao Liu, Chao Dong, Yu Qiao
    Accepted at TPAMI'21
    📜paper 💻code

  • Efficient Image Super-Resolution Using Pixel Attention
    Hengyuan Zhao, Xiangtao Kong, Jingwen He, Yu Qiao, Chao Dong
    Accepted at ECCVW'20
    📜paper 💻code

  • ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
    Xintao Wang, Ke Yu, Shixiang Wu, Jinjin Gu, Yihao Liu, Chao Dong, Yu Qiao, Chen Change Loy
    Accepted at ECCVW'18
    📜paper 💻code

  • Recovering Realistic Texture in Image Super-Resolution by Deep Spatial Feature Transform
    Accepted at CVPR'18
    Xintao Wang, Ke Yu, Chao Dong, Chen Change Loy
    📜paper 💻code

  • Accelerating the Super-Resolution Convolutional Neural Network
    Chao Dong, Chen Change Loy, Xiaoou Tang
    Accepted at ECCV'16
    📜paper 💻code

  • Image Super-Resolution Using Deep Convolutional Networks
    Chao Dong, Chen Change Loy, Kaiming He, Xiaoou Tang
    Accepted at TPAMI'16
    📜paper 🏠project

License

This project is released under the Apache 2.0 license.

Projects in Open-XSource

About

X-Super-Resolution is dedicated to presenting the research efforts of XPixel in the realm of image super-resolution.

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published