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...

Ausführliche Beschreibung

Bibliographische Detailangaben

Veröffentlicht in:	International journal of applied earth observation and geoinformation Jg. 144; S. 104885
Hauptverfasser:	Zhang, Ning, Wang, Yongcheng, Li, Gang, Xu, Dongdong, Werner, Martin
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 01.11.2025 Elsevier
Schlagworte:	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
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!