MISR-GAN

Model

The proposed model is an adaptation of ESRGAN with CA (Coordinate Attention) module adapted for an input as stack of multiple LR images

Datasets

PROBA-V

S2-NAIP

Training

To train a model on given dataset, run the following command, with the desired configuration file:

python -m ssr.train -opt ssr/options/*.yml

There are several sample configuration files in ssr/options/. Make sure the configuration file specifies correct paths to your downloaded data, the desired number of low-resolution input images, model parameters, and pretrained weights (if applicable).

Training process step:

1. Pre-training the model to minimize the pixel loss
2. GAN training the model with total loss (pixel + perceptual + adversarial)

Testing

To evaluate the model on a test set run the following command, with the desired configuration file:

python -m ssr.test -opt ssr/options/*.yml

Results

TODO (Future works)

• Finish the README.md
• Upload the final version of the code
• Train on bigger number of iterations
• Upload the weights

Acknowledgements

Thanks to these codebases for foundational Super-Resolution code and inspiration:

BasicSR

ESRGAN

Coordinate Attention

Contact

If you have any questions, please email yunseok.park@skoltech.ru or open an issue.