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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...
| Autores principales: | , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Springer US
2019
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| Materias: | |
| 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 |