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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

• Representative Work
  • SRCNN
  • RealESRGAN
• Blind SR
• Classic SR

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

• X-Super Resolution: Algorithms in the realm of image super-resolution.
• X-Image Processing: Algorithms in the realm of image restoration and enhancement.
• X-Video Processing: Algorithms for processing videos.
• X-Low level Interpretation: Algorithms for interpreting the principle of neural networks in low-level vision field.
• X-Evaluation and Benchmark: Datasets for training or evaluating state-of-the-art algorithms.