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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...
| Autores principales: | , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Springer US
2022
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| Materias: | |
| 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 |