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.
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Learning a Deep Convolutional Network for Image Super-Resolution
Chao Dong, Chen Change Loy, Kaiming He, Xiaoou Tang
Accepted at ECCV'14
📜paper🏠projectmore
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.
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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💻codemore
In this work, we extend the powerful ESRGAN to a practical restoration application, which is trained with pure synthetic data. Specifically:- A high-order degradation modeling process is introduced to better simulate complex real-world degradations.
- We also consider the common ringing and overshoot artifacts in the synthesis process.
- 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.
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Blind Image Super-Resolution: A Survey and Beyond
Anran Liu, Yihao Liu, Jinjin Gu, Yu Qiao, Chao Dong
Accepted at TPAMI'22
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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
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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
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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
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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
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Reflash Dropout in Image Super-Resolution
Xiangtao Kong, Xina Liu, Jinjin Gu, Yu Qiao, Chao Dong
Accepted at CVPR'22
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Suppressing Model Overfitting for Image Super-Resolution Networks
Ruicheng Feng, Jinjin Gu, Yu Qiao, Chao Dong
Accepted at CVPRW'19
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Blind Super-Resolution With Iterative Kernel Correction
Jinjin Gu, Hannan Lu, Wangmeng Zuo, Chao Dong
Accepted at CVPR'19
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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
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Activating More Pixels in Image Super-Resolution Transformer
Xiangyu Chen, Xintao Wang, Jiantao Zhou, Yu Qiao, Chao Dong
Accepted at CVPR'23
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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
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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
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ClassSR: A General Framework to Accelerate Super-Resolution Networks by Data Characteristic
Xiangtao Kong, Hengyuan Zhao, Yu Qiao, Chao Dong
Accepted at CVPR'21
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RankSRGAN: Super Resolution Generative Adversarial Networks with Learning to Rank
Wenlong Zhang, Yihao Liu, Chao Dong, Yu Qiao
Accepted at TPAMI'21
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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
This project is released under the Apache 2.0 license.
- 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.