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Training

  1. To download GoPro training and testing data, run
python download_data.py --data train-test
  1. Generate image patches from full-resolution training images of GoPro dataset
python generate_patches_gopro.py
  1. To pretrain DiffIR_S1, run
sh trainS1.sh
  1. To train DiffIR_S2, run
#set the 'pretrain_network_g' and 'pretrain_network_S1' in ./options/train_DiffIRS2.yml to be the path of DiffIR_S1's pre-trained model

sh trainS2.sh

Note: The above training script uses 8 GPUs by default.

Evaluation

Download the pre-trained model and place it in ./experiments/

Testing on GoPro dataset

  • Download GoPro testset, run
python download_data.py --data test --dataset GoPro
  • Testing
# modify the dataset path in ./options/test_DiffIRS2.yml

sh test.sh

Testing on HIDE dataset

  • Download HIDE testset, run
python download_data.py --data test --dataset HIDE
  • Testing
# modify the dataset path in ./options/test_DiffIRS2.yml

sh test.sh

To reproduce PSNR/SSIM scores of the paper on GoPro and HIDE datasets, run this MATLAB script

evaluate_gopro_hide.m