A Convex Variational Model for Learning Convolutional Image Atoms from Incomplete Data

A variational model for learning convolutional image atoms from corrupted and/or incomplete data is introduced and analyzed both in function space and numerically. Building on lifting and relaxation strategies, the proposed approach is convex and allows for simultaneous image reconstruction and atom...

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Detalles Bibliográficos
Autores principales: Chambolle, A., Holler, M., Pock, T.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2019
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7138786/
https://www.ncbi.nlm.nih.gov/pubmed/32300265
http://dx.doi.org/10.1007/s10851-019-00919-7

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7138786/
https://www.ncbi.nlm.nih.gov/pubmed/32300265
http://dx.doi.org/10.1007/s10851-019-00919-7

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