Work

Algorithmic unfolding for image reconstruction and localization problems in single-molecule fluorescence microscopy

Year: 2025

Type: article

Abstract: We propose an unfolded accelerated projected-gradient descent procedure to estimate model and algorithmic parameters for super-resolution and molecule localization problems in fluorescence 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 details, a standard $\ell _{2}$ cost is considered for image reconstruction (e.g. deconvolution/super-resolution, semi-blind deconvolution) problems, while a smoothed $\ell _{1}$ loss with learned binarization 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 both synthetic and benchmark images from the ISBI datasets.

Source: IMA Journal of Applied Mathematics

DOI: 10.1093/imamat/hxaf025

Authors: Silvia Bonettini, Luca Calatroni, Danilo Pezzi, Marco Prato

Institutions: University of Modena and Reggio Emilia, University of Genoa

Language: English

Topic: Image and Signal Denoising Methods

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,718

Funders: Istituto Nazionale di Alta Matematica "Francesco Severi", European Commission, Agence Nationale de la Recherche, Gruppo Nazionale per il Calcolo Scientifico