Remote Sensing Image Dehazing through an Unsupervised Generative Adversarial Network

The degradation of visual quality in remote sensing images caused by haze presents significant challenges in interpreting and extracting essential information. To effectively mitigate the impact of haze on image quality, we propose an unsupervised generative adversarial network specifically designed...

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Detalles Bibliográficos
Autores principales: Zhao, Liquan, Yin, Yanjiang, Zhong, Tie, Jia, Yanfei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10490768/
https://www.ncbi.nlm.nih.gov/pubmed/37687940
http://dx.doi.org/10.3390/s23177484

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10490768/
https://www.ncbi.nlm.nih.gov/pubmed/37687940
http://dx.doi.org/10.3390/s23177484

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