# DASR

**Repository Path**: huangfu-shimin/DASR

## Basic Information

- **Project Name**: DASR
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-10-12
- **Last Updated**: 2025-10-12

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# DASR
Pytorch implementation of "Unsupervised Degradation Representation Learning for Blind Super-Resolution", CVPR 2021

[[arXiv]](http://arxiv.org/pdf/2104.00416) [[CVF]](https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Unsupervised_Degradation_Representation_Learning_for_Blind_Super-Resolution_CVPR_2021_paper.pdf) [[Supp]](https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_Unsupervised_Degradation_Representation_CVPR_2021_supplemental.pdf)


## Overview

<p align="center"> <img src="Figs/fig.1.png" width="50%"> </p>


<p align="center"> <img src="Figs/fig.2.png" width="100%"> </p>


## Requirements
- Python 3.6
- PyTorch == 1.1.0
- numpy
- skimage
- imageio
- matplotlib
- cv2


## Train
### 1. Prepare training data 

1.1 Download the [DIV2K](https://data.vision.ee.ethz.ch/cvl/DIV2K/)  dataset and the [Flickr2K](http://cv.snu.ac.kr/research/EDSR/Flickr2K.tar) dataset.

1.2 Combine the HR images from these two datasets in `your_data_path/DF2K/HR` to build the DF2K dataset. 

### 2. Begin to train
Run `./main.sh` to train on the DF2K dataset. Please update `dir_data` in the bash file as `your_data_path`.


## Test
### 1. Prepare test data 
Download [benchmark datasets](https://github.com/xinntao/BasicSR/blob/a19aac61b277f64be050cef7fe578a121d944a0e/docs/Datasets.md) (e.g., Set5, Set14 and other test sets) and prepare HR/LR images in `your_data_path/benchmark`.


### 2. Begin to test
Run `./test.sh` to test on benchmark datasets. Please update `dir_data` in the bash file as `your_data_path`.


## Quick Test on An LR Image
Run `./quick_test.sh` to test on an LR image. Please update `img_dir` in the bash file as `your_img_path`.

## Visualization of Degradation Representations
<p align="center"> <img src="Figs/fig.6.png" width="50%"> </p>

## Comparative Results
### Noise-Free Degradations with Isotropic Gaussian Kernels

<p align="center"> <img src="Figs/tab2.png" width="100%"> </p>

<p align="center"> <img src="Figs/fig.5.png" width="100%"> </p>


### General Degradations with Anisotropic Gaussian Kernels and Noises
<p align="center"> <img src="Figs/tab3.png" width="100%"> </p>

<p align="center"> <img src="Figs/fig.7.png" width="100%"> </p>

### Unseen Degradations 

<p align="center"> <img src="Figs/fig.III.png" width="50%"> </p>

### Real Degradations (AIM real-world SR challenge)

<p align="center"> <img src="Figs/fig.VII.png" width="50%"> </p>

## Citation
```
@InProceedings{Wang2021Unsupervised,
  author    = {Wang, Longguang and Wang, Yingqian and Dong, Xiaoyu and Xu, Qingyu and Yang, Jungang and An, Wei and Guo, Yulan},
  title     = {Unsupervised Degradation Representation Learning for Blind Super-Resolution},
  booktitle = {CVPR},
  year      = {2021},
}
```

## Acknowledgements
This code is built on [EDSR (PyTorch)](https://github.com/thstkdgus35/EDSR-PyTorch), [IKC](https://github.com/yuanjunchai/IKC) and [MoCo](https://github.com/facebookresearch/moco). We thank the authors for sharing the codes.