Article Dans Une Revue PNAS Nexus Année : 2024

A Primal-Dual Data-Driven Method for Computational Optical Imaging with a Photonic Lantern

Résumé

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.

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hal-04913094 , version 1 (27-01-2025)

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Carlos Santos, Mathilde Larchevêque, Solal O'Sullivan, Martin van Waerebeke, Robert R Thomson, et al.. A Primal-Dual Data-Driven Method for Computational Optical Imaging with a Photonic Lantern. PNAS Nexus, 2024, 3 (4), pgae164. ⟨10.1093/pnasnexus/pgae164⟩. ⟨hal-04913094⟩
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