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Real-World Super-Resolution via Kernel Estimation and Noise Injection
| Date | Stars |
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| 2026-07-25 | 824 |
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# RealSR
Real-World Super-Resolution via Kernel Estimation and Noise Injection
Xiaozhong Ji, Yun Cao, Ying Tai, Chengjie Wang, Jilin Li, and Feiyue Huang
*Tencent YouTu Lab*
Our solution is the **winner of CVPR NTIRE 2020 Challenge on Real-World Super-Resolution** in both tracks.
(*Official PyTorch Implementation*)
## Update - Sep 2, 2020
Training code is available at https://github.com/Tencent/Real-SR
## Update - May 26, 2020
- Add [DF2K-JPEG](https://drive.google.com/open?id=1w8QbCLM6g-MMVlIhRERtSXrP-Dh7cPhm) Model.
- [Executable files](https://drive.google.com/open?id=1-FZPyMtuDfEnAPgSBfePYhv0NorznDPU) based on [ncnn](https://github.com/Tencent/ncnn) are available. Test your own images on windows/linux/macos. More details refer to [realsr-ncnn-vulkan](https://github.com/nihui/realsr-ncnn-vulkan)
- Usage - ```./realsr-ncnn-vulkan -i in.jpg -o out.png```
- ```-x``` - use ensemble
- ```-g 0``` - select gpu id.
## Introduction
Recent state-of-the-art super-resolution methods have achieved impressive performance on ideal datasets regardless of blur and noise. However, these methods always fail in real-world image super-resolution, since most of them adopt simple bicubic downsampling from high-quality images to construct Low-Resolution (LR) and High-Resolution (HR) pairs for training which may lose track of frequency-related details. To address this issue, we focus on designing a novel degradation framework for real-world images by estimating various blur kernels as well as real noise distributions. Based on our novel degradation framework, we can acquire LR images sharing a common domain with real-world images. Then, we propose a real-world super-resolution model aiming at better perception. Extensive experiments on synthetic noise data and real-world images demonstrate that our method outperforms the state-of-the-art methods, resulting in lower noise and better visual quality. In addition, our method is the winner of NTIRE 2020 Challenge on both tracks of Real-World Super-Resolution, which significantly outperforms other competitors by large margins.
If you are interested in this work, please cite our [paper](http://openaccess.thecvf.com/content_CVPRW_2020/papers/w31/Ji_Real-World_Super-Resolution_via_Kernel_Estimation_and_Noise_Injection_CVPRW_2020_paper.pdf)
@InProceedings{Ji_2020_CVPR_Workshops,
author = {Ji, Xiaozhong and Cao, Yun and Tai, Ying and Wang, Chengjie and Li, Jilin and Huang, Feiyue},
title = {Real-World Super-Resolution via Kernel Estimation and Noise Injection},
booktitle = {The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2020}
}
and challenge report [NTIRE 2020 Challenge on Real-World Image Super-Resolution: Methods and Results](https://arxiv.org/pdf/2005.01996.pdf)
@article{Lugmayr2020ntire,
title={NTIRE 2020 Challenge on Real-World Image Super-Resolution: Methods and Results},
author={Andreas Lugmayr, Martin Danelljan, Radu Timofte, Namhyuk Ahn, Dongwoon Bai, Jie Cai, Yun Cao, Junyang Chen, Kaihua Cheng, SeYoung Chun, Wei Deng, Mostafa El-Khamy Chiu, Man Ho, Xiaozhong Ji, Amin Kheradmand, Gwantae Kim, Hanseok Ko, Kanghyu Lee, Jungwon Lee, Hao Li, Ziluan Liu, Zhi-Song Liu, Shuai Liu, Yunhua Lu, Zibo Meng, Pablo Navarrete, Michelini Christian, Micheloni Kalpesh, Prajapati Haoyu, Ren Yong, Hyeok Seo, Wan-Chi Siu, Kyung-Ah Sohn, Ying Tai, Rao Muhammad Umer, Shuangquan Wang, Huibing Wang, Timothy Haoning Wu, Haoning Wu, Biao Yang, Fuzhi Yang, Jaejun Yoo, Tongtong Zhao, Yuanbo Zhou, Haijie Zhuo, Ziyao Zong, Xueyi Zou},
journal={CVPR Workshops},
year={2020},
}
## Visual Results
# Quantitative Results Compared with Other Participating Methods
'Impressionism' is our teamExcerpt of 6,462 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:3b17b1bf2091de13, topic:super-resolution, desc:super resolution, readme:super resolution