Undersampled Phase Retrieval with Image Priors
Fuente:
arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866908545835859968 |
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| author | Ducotterd, Stanislas Hu, Zhiyuan Unser, Michael Dong, Jonathan |
| author_facet | Ducotterd, Stanislas Hu, Zhiyuan Unser, Michael Dong, Jonathan |
| contents | Phase retrieval seeks to recover a complex signal from amplitude-only measurements, a challenging nonlinear inverse problem. Current theory and algorithms often ignore signal priors. By contrast, we evaluate here a variety of image priors in the context of severe undersampling with structured random Fourier measurements. Our results show that those priors significantly improve reconstruction, allowing accurate reconstruction even below the weak recovery threshold. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_15026 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Undersampled Phase Retrieval with Image Priors Ducotterd, Stanislas Hu, Zhiyuan Unser, Michael Dong, Jonathan Image and Video Processing Machine Learning Phase retrieval seeks to recover a complex signal from amplitude-only measurements, a challenging nonlinear inverse problem. Current theory and algorithms often ignore signal priors. By contrast, we evaluate here a variety of image priors in the context of severe undersampling with structured random Fourier measurements. Our results show that those priors significantly improve reconstruction, allowing accurate reconstruction even below the weak recovery threshold. |
| title | Undersampled Phase Retrieval with Image Priors |
| topic | Image and Video Processing Machine Learning |
| url | https://arxiv.org/abs/2509.15026 |