Our new work about blind image super-resolution has been accepted to IJCV. The paper is available at End-to-end Alternating Optimization for Real-World Blind Super Resolution. The codes are released at RealDAN.

This is an official implementation of Unfolding the Alternating Optimization for Blind Super Resolution and End-to-end Alternating Optimization for Blind Super Resolution

If this repo works for you, please cite our papers:

@article{luo2020unfolding,
  title={Unfolding the Alternating Optimization for Blind Super Resolution},
  author={Luo, Zhengxiong and Huang, Yan and Li, Shang and Wang, Liang and Tan, Tieniu},
  journal={Advances in Neural Information Processing Systems (NeurIPS)},
  volume={33},
  year={2020}
}
@misc{luo2021endtoend,
      title={End-to-end Alternating Optimization for Blind Super Resolution}, 
      author={Zhengxiong Luo and Yan Huang and Shang Li and Liang Wang and Tieniu Tan},
      year={2021},
      eprint={2105.06878},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

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

News

[x] Add more pretrained weights and update the results of DANv1 !
[x] Add pretrained weights and update the results of about [IKC]!
[x] Add DANv2 !!!

Main Results

Results about Setting 1

| Method | Scale | Set5 | Set5 | Set14 | Set14 | B100 | B100 | Urban100 | Urban100 | Mangan109 | Manga109 | |:------:|:-----:|:-------:|:--------:|:-------:|---------|:-------:|----------|:-------:|-----------|:-------:|:-------:| | | | PSNR | SSIM | PSNR | SSIM | PSNR | SSIM | PSNR | SSIM | PSNR | SSIM | | IKC | x2 | 37.19 | 0.9526 | 32.94 | 0.9024 | 31.51 | 0.8790 | 29.85 | 0.8928 | 36.93 | 0.9667 | | DANv1 | x2 | 37.34 | 0.9526 | 33.08 | 0.9041 | 31.76 | 0.8858 | 30.60 | 0.9060 | 37.23 | 0.9710 | | DANv2 | x2 | 37.60 | 0.9544 | 33.44 | 0.9094 | 32.00 | 0.8904 | 31.43 | 0.9174 | 38.07 | 0.9734 | | IKC | x3 | 33.06 | 0.9146 | 29.38 | 0.8233 | 28.53 | 0.7899 | 27.43 | 0.8302 | 32.43 | 0.9316 | | DANv1 | x3 | 34.04 | 0.9199 | 30.09 | 0.8287 | 28.94 | 0.7919 | 27.65 | 0.8352 | 33.16 | 0.9382 | | DANv2 | x3 | 34.19 | 0.9209 | 30.20 | 0.8309 | 29.03 | 0.7948 | 27.83 | 0.8395 | 33.28 | 0.9400 | | IKC | x4 | 31.67 | 0.8829 | 28.31 | 0.7643 | 27.37 | 0.7192 | 25.33 | 0.7504 | 28.91 | 0.8782 | | DANv1 | x4 | 31.89 | 0.8864 | 28.42 | 0.7687 | 27.51 | 0.7248 | 25.86 | 0.7721 | 30.50 | 0.9037 | | DANv2 | x4 | 32.00 | 0.8885 | 28.50 | 0.7715 | 27.56 | 0.7277 | 25.94 | 0.7748 | 30.45 | 0.9037 |

Results about Setting 2 (DIV2KRK)

|Method | x2 | x2 | x4 | x4
|:--------------:|:-----:|:------:|:-----:|:------:| | | PSNR | SSIM | PSNR | SSIM | |KernelGAN + ZSSR| 30.36 | 0.8669 | 26.81 | 0.7316 | |DANv1 | 32.56 | 0.8997 | 27.55 | 0.7582 | |DANv2 | 32.58 | 0.9048 | 28.74 | 0.7893 |

Dependenices

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

Pretrained Weights

Pretrained weights of DANv1 and IKC are available at BaiduYun(Password: cbjv) or GoogleDrive. Download the weights to checkpoints

.
|-- checkpoints
`-- |-- DANv1
    |   |-- ...
    |-- DANv2
    |   |-- ...
    `--IKC
        |-- ...

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/DANv1
python3 train.py -opt=options/setting1/train_setting1_x4.yml

For distributed training

cd codes/config/DANv1
python3 -m torch.distributed.launch --nproc_per_node=8 --master_poer=4321 train.py -opt=options/setting1/train_setting1_x4.yml --launcher pytorch

Test on Synthetic Images

cd codes/config/DANv1
python3 test.py -opt=options/setting1/test_setting1_x4.yml

Test on Real Images

cd codes/config/DANv1
python3 inference.py -input_dir=/path/to/real/images/ -output_dir=/path/to/save/sr/results/

DAN

Install / Use

README