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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.

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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

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Additional Info

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