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:

• PyTorch>1.10
• OpenCV
• Matplotlib 3.3.4
• opencv-python
• pyyaml
• tqdm
• numpy
• torchvision

• Download pretrained models, and copy them to ./train_logs/:

• 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.

• Step 1, the following cmd will report a performance evaluated with python script, and generated images are placed in ./SR

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

• Step2, please execute the 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}
}