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arXiv · 2403.17506

Algorithmic unfolding for image reconstruction and localization problems in fluorescence microscopy

Silvia Bonettini·Luca Calatroni·Danilo Pezzi·Marco Prato
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Abstract

We propose an unfolded accelerated projected-gradient descent procedure to estimate model and algorithmic parameters for image super-resolution and molecule localization problems in image microscopy. The variational lower-level constraint enforces sparsity of the solution and encodes different noise statistics (Gaussian, Poisson), while the upper-level cost assesses optimality w.r.t.~the task considered. In more detail, a standard $\ell_2$ cost is considered for image reconstruction (e.g., deconvolution/super-resolution, semi-blind deconvolution) problems, while a smoothed $\ell_1$ is employed to assess localization precision in some exemplary fluorescence microscopy problems exploiting single-molecule activation. Several numerical experiments are reported to validate the proposed approach on synthetic and realistic ISBI data.

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Silvia Bonettini, Luca Calatroni, Danilo Pezzi, Marco Prato. 2024-03-26. Algorithmic unfolding for image reconstruction and localization problems in fluorescence microscopy. https://arxiv.org/abs/2403.17506

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