Algorithmic unfolding for image reconstruction and localization problems in fluorescence microscopy

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...

Full description

Saved in:
Bibliographic Details
Main Authors Bonettini, Silvia, Calatroni, Luca, Pezzi, Danilo, Prato, Marco
Format Journal Article
LanguageEnglish
Published 26.03.2024
Subjects
Computer Science - Numerical Analysis
Mathematics - Numerical Analysis
Online AccessGet full text

Cover

More Information
Summary: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.
DOI:10.48550/arxiv.2403.17506