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Pré-Publication, Document De Travail Année : 2024

Algorithmic unfolding for image reconstruction and localization problems in fluorescence microscopy

Résumé

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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Dates et versions

hal-04527400 , version 1 (30-03-2024)

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  • HAL Id : hal-04527400 , version 1

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Silvia Bonettini, Luca Calatroni, Danilo Pezzi, Marco Prato. Algorithmic unfolding for image reconstruction and localization problems in fluorescence microscopy. 2024. ⟨hal-04527400⟩
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