The official repository with Pytorch
Our paper can be downloaded from [Arxiv].
Clone this repo:
git clone https://github.com/Francis0625/OmniSR.git
cd OmniSR
Dependencies:
./train_logs/
: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 |
./benchmark/
. If you want to generate the benchmark by yourself, please refer to the official repository of RCAN../SR
python test.py -v "OmniSR_X4_DF2K" -s 994 -t tester_Matlab --test_dataset_name "Urban100"
Evaluate_PSNR_SSIM.m
script in the root directory to obtain the results reported in the paper. Please modify Line 8 (Evaluate_PSNR_SSIM.m): methods = {'OmniSR_X4_DF2K'};
and Line 10 (Evaluate_PSNR_SSIM.m): dataset = {'Urban100'};
to match the model/dataset name evaluated above.Step1, please download training dataset from DIV2K (Train Data Track 1 bicubic downscaling x? (LR images)
and Train Data (HR images)
), then set the dataset root path in ./env/env.json: Line 8: "DIV2K":"TO YOUR DIV2K ROOT PATH"
Step2, please download benchmark (baidu cloud (passwd: sjtu) , Google driver), and copy them to ./benchmark/
. If you want to generate the benchmark by yourself, please refer to the official repository of RCAN.
Step3, training with DIV2K $\times 4$ dataset:
python train.py -v "OmniSR_X4_DIV2K" -p train --train_yaml "train_OmniSR_X4_DIV2K.yaml"
result.tex is the corresponding tex code for result comparison.
This project is released under the Apache 2.0 license.
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}
}