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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.

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