Francis0625 / Omni-SR

[CVPR2023] Implementation of ''Omni Aggregation Networks for Lightweight Image Super-Resolution".
191 stars 12 forks source link

Omni Aggregation Networks for Lightweight Image Super-Resolution (OmniSR)

Accepted by CVPR2023

The official repository with Pytorch

Our paper can be downloaded from [Arxiv].

Installation

Clone this repo:

git clone https://github.com/Francis0625/OmniSR.git
cd OmniSR

Dependencies:

Preparation

Settings CKPT name CKPT url
DIV2K $\times 2$ OmniSR_X2_DIV2K.zip baidu cloud (passwd: sjtu) , Google driver
DF2K $\times 2$ OmniSR_X2_DF2K.zip baidu cloud (passwd: sjtu) , Google driver
DIV2K $\times 3$ OmniSR_X3_DIV2K.zip baidu cloud (passwd: sjtu) , Google driver
DF2K $\times 3$ OmniSR_X3_DF2K.zip baidu cloud (passwd: sjtu) , Google driver
DIV2K $\times 4$ OmniSR_X4_DIV2K.zip baidu cloud (passwd: sjtu) , Google driver
DF2K $\times 4$ OmniSR_X4_DF2K.zip baidu cloud (passwd: sjtu) , Google driver

Evaluate Pretrained Models

Example: evaluate the model trained with DF2K@X4:

python test.py -v "OmniSR_X4_DF2K" -s 994 -t tester_Matlab --test_dataset_name "Urban100"

Training

Visualization

performance

Results

performance result.tex is the corresponding tex code for result comparison.

Related Projects

License

This project is released under the Apache 2.0 license.

To cite our paper

If this work helps your research, please cite the following paper:

@inproceedings{omni_sr,
  title      = {Omni Aggregation Networks for Lightweight Image Super-Resolution},
  author     = {Wang, Hang and Chen, Xuanhong and Ni, Bingbing and Liu, Yutian and Liu jinfan},
  booktitle  = {Conference on Computer Vision and Pattern Recognition},
  year       = {2023}
}