A variational autoencoder inspired unsupervised remote sensing image super resolution method with multi-degradation

In current super-resolution (SR) research, blind SR models capable of handling multiple degradations have attracted significant attention. Inspired by variational autoencoders (VAEs) that model data distributions through latent representations, this paper proposes a VAE framework for unsupervised re...

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Vydané v:	International journal of applied earth observation and geoinformation Ročník 144; s. 104885
Hlavní autori:	Zhang, Ning, Wang, Yongcheng, Li, Gang, Xu, Dongdong, Werner, Martin
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier B.V 01.11.2025 Elsevier
Predmet:	Multi-degradation Remote sensing image (RSI) Super-resolution (SR) Unsupervised learning Variational autoencoder (VAE) Variational autoencoder (VAE) Multi-degradation Super-resolution (SR) Unsupervised learning Remote sensing image (RSI)
ISSN:	1569-8432
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