A Generative Adversarial Network-Based Image Denoiser Controlling Heterogeneous Losses

We propose a novel generative adversarial network (GAN)-based image denoising method that utilizes heterogeneous losses. In order to improve the restoration quality of the structural information of the generator, the heterogeneous losses, including the structural loss in addition to the conventional...

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Detalles Bibliográficos
Autores principales: Cho, Sung In, Park, Jae Hyeon, Kang, Suk-Ju
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7915760/
https://www.ncbi.nlm.nih.gov/pubmed/33567620
http://dx.doi.org/10.3390/s21041191

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7915760/
https://www.ncbi.nlm.nih.gov/pubmed/33567620
http://dx.doi.org/10.3390/s21041191

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