Constrained and unconstrained deep image prior optimization models with automatic regularization

Deep Image Prior (DIP) is currently among the most efficient unsupervised deep learning based methods for ill-posed inverse problems in imaging. This novel framework relies on the implicit regularization provided by representing images as the output of generative Convolutional Neural Network (CNN) a...

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Detalles Bibliográficos
Autores principales: Cascarano, Pasquale, Franchini, Giorgia, Kobler, Erich, Porta, Federica, Sebastiani, Andrea
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9326425/
https://www.ncbi.nlm.nih.gov/pubmed/35909881
http://dx.doi.org/10.1007/s10589-022-00392-w

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9326425/
https://www.ncbi.nlm.nih.gov/pubmed/35909881
http://dx.doi.org/10.1007/s10589-022-00392-w

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