BSRN

Blueprint Separable Residual Network for Efficient Image Super-Resolution
Zheyuan Li, Yingqi Liu, Xiangyu Chen, Haoming Cai, Jinjin Gu, Yu Qiao, Chao Dong

BibTex

@InProceedings{Li_2022_CVPR,
    author    = {Li, Zheyuan and Liu, Yingqi and Chen, Xiangyu and Cai, Haoming and Gu, Jinjin and Qiao, Yu and Dong, Chao},
    title     = {Blueprint Separable Residual Network for Efficient Image Super-Resolution},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
    month     = {June},
    year      = {2022},
    pages     = {833-843}
}

Environment

PyTorch >= 1.7
BasicSR >= 1.3.4.9

Installation

pip install -r requirements.txt
python setup.py develop

How To Test

· Refer to ./options/test for the configuration file of the model to be tested, and prepare the testing data and pretrained model.
· The pretrained models are available in ./experiments/pretrained_models/
· Then run the follwing codes (taking net_g_BSRN_x4.pth as an example):

python basicsr/test.py -opt options/test/benchmark_BSRN_x4.yml

The testing results will be saved in the ./results folder.

How To Train

· Refer to ./options/train for the configuration file of the model to train.
· Preparation of training data can refer to this page. All datasets can be downloaded at the official website.
· Note that the default training dataset is based on lmdb, refer to docs in BasicSR to learn how to generate the training datasets.
· The training command is like

CUDA_VISIBLE_DEVICES=0 python basicsr/train.py -opt options/train/train_BSRN_x4.yml
CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nproc_per_node=4 --master_port=4321 basicsr/train.py -opt options/train/train_BSRN-S_x4.yml --launcher pytorch

For more training commands and details, please check the docs in BasicSR

Results

The inference results on benchmark datasets are available at Google Drive or Baidu Netdisk (access code: VISU).

Contact

If you have any question, please email [email protected] or join in the Wechat group of BasicSR to discuss with the authors.