ReDSR

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Official codes for 'Preserving Full Degradation Details for Blind Image Super-Resolution'

// repository documentation

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# Preserving Full Degradation Details for Blind Image Super-Resolution

Pytorch implementation of paper *Preserving Full Degradation Details for Blind Image Super-Resolution*.

![image](https://raw.githubusercontent.com/Chernobyllight/ReDSR/master/framework/framework.png)

## Installation

- Install python 3.8.0, torch 2.0.0, CUDA 11.7 and other essential packages (Note that using other versions of packages may affect performance).
- Clone this repo

```
git clone https://github.com/Chernobyllight/ReDSR
cd ReDSR
```

## Test

test setting1: model trained only on isotropic Gaussian blur kernels

test setting2: model trained on anisotropic Gaussian blur kernels and additive Gaussian noise

- Get into evaluation codes folder './TEST/TEST_setting1'

```
  cd ./TEST/TEST_setting1
  ```

- We provide pretrained models in './checkpoints/'. Before testing, specify the model checkpoint in 'test.yml'.

```
  DANET_CHECKPOINT: './checkpoints/setting1/setting1_ckp.tar'
  ```

Or change the path with your own absolute path

- Run 'test.py'

```
  python test.py
  ```

## Train

- Get into training codes folder './TRAIN/TRAIN_setting1'

```
  cd ./TRAIN/TRAIN_setting1
  ```

- Specify the dataset path in 'train.yml'

```
  data:
  	clean_dir: './datasets/div2k'
  ```

Or change the path with your own dataset absolute path

- Run 'train.py'

```
  python train.py
  ```

ReDSR

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