README.md

Official implementation of paper "Retinal Image Restoration using Transformer and Cycle-Consistent Generative Adversarial Network"

Vision Transformer and Convolutional Neural Network Cycle-Consistent Generative Adversarial

1. Create anaconda environment using the following code: "conda env create -f env.yml".

2. Download EyeQ and/or Mendeley datasets and place them in datasets folder accordingly.

3. Run Train.py selecting the dataset.

4. Run Test.py selecting the dataset.

Model weights are available HERE.

Citing

If you use this code, please use the following BibTeX entry.

@misc{https://doi.org/10.48550/arxiv.2303.01939,
  doi = {10.48550/ARXIV.2303.01939},
  
  url = {https://arxiv.org/abs/2303.01939},
  
  author = {Alimanov, Alnur and Islam, Md Baharul},
  
  keywords = {Image and Video Processing (eess.IV), Computer Vision and Pattern Recognition (cs.CV), FOS: Electrical engineering, electronic engineering, information engineering, FOS: Electrical engineering, electronic engineering, information engineering, FOS: Computer and information sciences, FOS: Computer and information sciences},
  
  title = {Retinal Image Restoration using Transformer and Cycle-Consistent Generative Adversarial Network},
  
  publisher = {arXiv},
  
  year = {2023},
  
  copyright = {Creative Commons Attribution 4.0 International}
}