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https://github.com/jixiaozhong/RealSR
Real-World Super-Resolution via Kernel Estimation and Noise Injection
https://github.com/jixiaozhong/RealSR
kernel-estimation noise-injection super-resolution
Last synced: 4 months ago
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Real-World Super-Resolution via Kernel Estimation and Noise Injection
- Host: GitHub
- URL: https://github.com/jixiaozhong/RealSR
- Owner: jixiaozhong
- License: apache-2.0
- Created: 2020-04-30T08:49:01.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2020-09-02T03:00:05.000Z (over 4 years ago)
- Last Synced: 2023-11-07T15:29:52.854Z (about 1 year ago)
- Topics: kernel-estimation, noise-injection, super-resolution
- Language: Python
- Size: 14.3 MB
- Stars: 696
- Watchers: 26
- Forks: 97
- Open Issues: 26
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 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 team. Note that the final decision is based on MOS (Mean Opinion Score) and MOR (Mean Opinion Rank).
# Qualitative Results Compared with Other Participating Methods
'Impressionism' is our team.
## Dependencies and Installation
This code is based on [BasicSR](https://github.com/xinntao/BasicSR).
- Python 3 (Recommend to use [Anaconda](https://www.anaconda.com/download/#linux))
- [PyTorch >= 1.0](https://pytorch.org/)
- NVIDIA GPU + [CUDA](https://developer.nvidia.com/cuda-downloads)
- Python packages: `pip install numpy opencv-python lmdb pyyaml`
- TensorBoard:
- PyTorch >= 1.1: `pip install tb-nightly future`
- PyTorch == 1.0: `pip install tensorboardX`
## Pre-trained models
- Models for challenge results
- [DF2K](https://drive.google.com/open?id=1pWGfSw-UxOkrtbh14GeLQgYnMLdLguOF) for corrupted images with processing noise.
- [DPED](https://drive.google.com/open?id=1zZIuQSepFlupV103AatoP-JSJpwJFS19) for real images taken by cell phone camera.
- Extended models
- [DF2K-JPEG](https://drive.google.com/open?id=1w8QbCLM6g-MMVlIhRERtSXrP-Dh7cPhm) for compressed jpeg image.
## Testing
Download dataset from [NTIRE 2020 RWSR](https://competitions.codalab.org/competitions/22220#participate) and unzip it to your path.
For convenient, we provide [Corrupted-te-x](https://drive.google.com/open?id=1GrLxeE-LruddQoAePV1Z7MFclXdZWHMa) and [DPEDiphone-crop-te-x](https://drive.google.com/open?id=19zlofWRxkhsjf_TuRA2oI9jgozifGvxp).
```cd ./codes```
### DF2K: Image processing artifacts
1. Modify the configuration file options/df2k/test_df2k.yml
- line 1 : 'name' -- dir name for saving the testing results
- line 13 : 'dataroot_LR' -- test images dir
- line 26 : 'pretrain_model_G' -- pre-trained model for testing
2. Run command :
```CUDA_VISIBLE_DEVICES=X python3 test.py -opt options/df2k/test_df2k.yml ```
3. The output images is saved in '../results/'
### DPED: Smartphone images
1. Modify the configuration file options/dped/test_dped.yml
- line 1 : 'name' -- dir name for saving the testing results
- line 13 : 'dataroot_LR' -- test images dir
- line 26 : 'pretrain_model_G' -- pre-trained model for testing
2. Run command :
```CUDA_VISIBLE_DEVICES=X python3 test.py -opt options/dped/test_dped.yml```
3. The output images is saved in '../results/'
## Training code
Release soon.