RaSeedGAN: Randomly-SEEDed super-resolution GAN for sparse measurements

A novel deep-learning approach based on generative adversarial networks to perform super-resolution reconstruction of sparse measurements.

Data and Resources

Cite this as

Alejandro G¨uemes, Carlos Sanmiguel Vila, Stefano Discetti (2024). Dataset: RaSeedGAN: Randomly-SEEDed super-resolution GAN for sparse measurements. https://doi.org/10.57702/w5srv915

DOI retrieved: December 16, 2024

Additional Info

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Created December 16, 2024
Last update December 16, 2024
Defined In https://doi.org/10.48550/arXiv.2202.11701
Author Alejandro G¨uemes
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Carlos Sanmiguel Vila
Stefano Discetti
Homepage https://doi.org/10.5281/zenodo.7191210