We consider a variational model for Single Molecule Localisation Microscopy (SMLM) super-resolution. More specifically, we study a generalization of the Continuous Exact ell-{0} (CELO) penalty, recently introduced to relax the ell-{2}-ell-{0} problem, where a weighted-ell-{2} data fidelity now models signal-dependent Poisson noise. For the numerical solution of the associated non-convex minimisation problem, we propose an iterative reweighted ell-{1} (IRL1) algorithm, for which efficient parameter computation strategies are detailed. Both qualitative and quantitative molecule localisation results are reported, showing that the proposed weighted-CELO (wCELO) model for Poisson noisy data improves the results obtained by CELO and state-of-the art deep-learning approaches for the high-density SMLM ISBI 2013 dataset.

Weighted-celo sparse regularisation for molecule localisation in super-resolution microscopy with poisson data

Lazzaretti M.;Estatico C.
2021-01-01

Abstract

We consider a variational model for Single Molecule Localisation Microscopy (SMLM) super-resolution. More specifically, we study a generalization of the Continuous Exact ell-{0} (CELO) penalty, recently introduced to relax the ell-{2}-ell-{0} problem, where a weighted-ell-{2} data fidelity now models signal-dependent Poisson noise. For the numerical solution of the associated non-convex minimisation problem, we propose an iterative reweighted ell-{1} (IRL1) algorithm, for which efficient parameter computation strategies are detailed. Both qualitative and quantitative molecule localisation results are reported, showing that the proposed weighted-CELO (wCELO) model for Poisson noisy data improves the results obtained by CELO and state-of-the art deep-learning approaches for the high-density SMLM ISBI 2013 dataset.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11567/1101253
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