OW-SLR: Overlapping Windows on Semi-Local Region for Image Super-Resolution

Project Page | Paper | Data

This is the official implementation for OW-SLR: Overlapping Windows on Semi-Local Region for Image Super-Resolution. OW-SLR provides better results for classifying healthy and diseased retinal images such as diabetic retinopathy and normals from the given set of OCT-A images.

Training

Images to be trained are put in the "data" folder and validation images in the "val" folder.

python3 train.py --config train.yaml

Testing

python3 test.py --config train.yaml --batch_size 40000 --img_path 438.png --upscale_factor 4.5 --model_path save/train/epoch-last.pth

Please change the arguments mentioned above as per requirement. We have given an example for quick replication. Considering reducing the batch size if you run out of GPU memory.

Citation

If you find this code useful for your research, please use the following BibTeX entry.

@article{bhardwaj2023ow,
  title={OW-SLR: overlapping windows on semi-local region for image super-resolution},
  author={Bhardwaj, Rishav and Balaji, Janarthanam Jothi and Lakshminarayanan, Vasudevan},
  journal={Journal of Imaging},
  volume={9},
  number={11},
  pages={246},
  year={2023},
  publisher={MDPI}
}

Acknowledgments

Our project is benefit from these great resources:

• Learning Continuous Image Representation with Local Implicit Image Function

Thanks for their sharing code.