ofirkris / DAN

This is an official implementation of Unfolding the Alternating Optimization for Blind Super Resolution

This is an official implementation of Unfolding the Alternating Optimization for Blind Super Resolution

This repo is build on the basis of [MMSR] and [IKC]

Dependenices

• python3
• pytorch >= 1.5
• NVIDIA GPU + CUDA
• Python packages: pip3 install numpy opencv-python lmdb pyyaml

Models

The pretrained models and visual results on DIV2KRK are available at BaiduYun(Password: u9ah) GoogleDrive

Dataset Preparation

We use DIV2K and Flickr2K as our training datasets.

For evaluation of Setting 1, we use five datasets, i.e., Set5, Set14, Urban100, BSD100 and Manga109.

We use DIV2KRK for evaluation of Setting 2.

To train a model on the full dataset(DIV2K+Flickr2K, totally 3450 images), download datasets from official websites. After download, run codes/scripts/generate_mod_blur_LR_bic.py to generate LRblur/LR/HR/Bicubic datasets paths. (You need to modify the file paths by yourself.)

python3 codes/scripts/generate_mod_blur_LR_bic.py

For efficient IO, run codes/scripts/create_lmdb.py to transform datasets to binary files. (You need to modify the file paths by yourself.)

python3 codes/scripts/create_lmdb.py

Train

For single GPU:

cd codes/config/DAN
python3 train.py -opt=train_setting1.yml

For distributed training

cd codes/config/DAN
bash run_scripts.sh

Test on Synthetic Images

cd codes/config/DAN
python3 test.py -opt=test_setting1.yml

Test on Real Images

cd codes/config/DAN
python3 test_single_image.py -opt=test_option.yml -input_dir=/path/to/real/images/ -output_dir=/path/to/save/sr/results/