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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2306.11679 |
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| _version_ | 1866929316032413696 |
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| author | Garcia, Carlos Santos Larchevêque, Mathilde O'Sullivan, Solal Van Waerebeke, Martin Thomson, Robert R. Repetti, Audrey Pesquet, Jean-Christophe |
| author_facet | Garcia, Carlos Santos Larchevêque, Mathilde O'Sullivan, Solal Van Waerebeke, Martin Thomson, Robert R. Repetti, Audrey Pesquet, Jean-Christophe |
| contents | Optical fibres aim to image in-vivo biological processes. In this context, high spatial resolution and stability to fibre movements are key to enable decision-making processes (e.g., for microendoscopy). Recently, a single-pixel imaging technique based on a multicore fibre photonic lantern has been designed, named computational optical imaging using a lantern (COIL). A proximal algorithm based on a sparsity prior, dubbed SARA-COIL, has been further proposed to solve the associated inverse problem, to enable image reconstructions for high resolution COIL microendoscopy. In this work, we develop a data-driven approach for COIL. We replace the sparsity prior in the proximal algorithm by a learned denoiser, leading to a plug-and-play (PnP) algorithm. The resulting PnP method, based on a proximal primal-dual algorithm, enables to solve the Morozov formulation of the inverse problem. We use recent results in learning theory to train a network with desirable Lipschitz properties, and we show that the resulting primal-dual PnP algorithm converges to a solution to a monotone inclusion problem. Our simulations highlight that the proposed data-driven approach improves the reconstruction quality over variational SARA-COIL method on both simulated and real data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2306_11679 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | A primal-dual data-driven method for computational optical imaging with a photonic lantern Garcia, Carlos Santos Larchevêque, Mathilde O'Sullivan, Solal Van Waerebeke, Martin Thomson, Robert R. Repetti, Audrey Pesquet, Jean-Christophe Image and Video Processing Optical fibres aim to image in-vivo biological processes. In this context, high spatial resolution and stability to fibre movements are key to enable decision-making processes (e.g., for microendoscopy). Recently, a single-pixel imaging technique based on a multicore fibre photonic lantern has been designed, named computational optical imaging using a lantern (COIL). A proximal algorithm based on a sparsity prior, dubbed SARA-COIL, has been further proposed to solve the associated inverse problem, to enable image reconstructions for high resolution COIL microendoscopy. In this work, we develop a data-driven approach for COIL. We replace the sparsity prior in the proximal algorithm by a learned denoiser, leading to a plug-and-play (PnP) algorithm. The resulting PnP method, based on a proximal primal-dual algorithm, enables to solve the Morozov formulation of the inverse problem. We use recent results in learning theory to train a network with desirable Lipschitz properties, and we show that the resulting primal-dual PnP algorithm converges to a solution to a monotone inclusion problem. Our simulations highlight that the proposed data-driven approach improves the reconstruction quality over variational SARA-COIL method on both simulated and real data. |
| title | A primal-dual data-driven method for computational optical imaging with a photonic lantern |
| topic | Image and Video Processing |
| url | https://arxiv.org/abs/2306.11679 |