DSSR

This is a pytorch implementation of Learning Detail-Structure Alternative Optimization for Blind Super-Resolution. This repo is built on the basis of DAN, thanks for their open-sourcing!

Requirement

• python3
• NVIDIA GPU + CUDA
• pytorch >= 1.7.1
• python packages: pip3 install numpy opencv-python lmdb pyyaml

Quick Start

Download the pretrained models and put them into checkpoints folder. For different settings, you may still have to modify the options/test_setting.yml.

python inference.py -input_dir=<your_input_dir> -output_dir=<your_output_dir>

Test

There are two blind settings mentioned in our paper. For setting1, we synthesize the Gaussian8 datasets using scripts/generate_mod_blur_LR_bic.py with five datasets: Set5, Set14, BSD100, Urban100, Manga109. Download the benchmark datasets and put them into datasets folder like this (only put the ground-truth images)

datasets
|── Set5
|── Set14
|── ...

Then run the script generate_mod_blur_LR_bic.py, and you will get folder like this

datasets
|── Set5
|── Set5G8
|   |── LRblur
|   |── HR
|── Set14
|── ...

For setting2, we using the benchmark dataset DIV2KRK from KernelGAN.

Modify the dataset path and test settings in options/test_setting.yml and run the following command

python test.py -opt=options/test_setting.yml

Train

Download the DIV2K and Flickr2K and merge it into one folder. Modify options/train_setting.yml and run the following command

python train.py -opt=options/train_setting.yml

Citation

If you find this repo useful, please consider citing our work:

@ARTICLE{9721549,
  author={Li, Feng and Wu, Yixuan and Bai, Huihui and Lin, Weisi and Cong, Runmin and Zhang, Chunjie and Zhao, Yao},
  journal={IEEE Transactions on Multimedia}, 
  title={Learning Detail-Structure Alternative Optimization for Blind Super-Resolution}, 
  year={2022},
  volume={},
  number={},
  pages={1-1},
  doi={10.1109/TMM.2022.3152090}}