Single molecule localization by ${\ell }_2-{\ell }_0$ constrained optimization

Abstract

Single Molecule Localization Microscopy (SMLM) enables the acquisition of high-resolution images by alternating between activation of a sparse subset of fluorescent molecules present in a sample and localization. In this work, the localization problem is formulated as a constrained sparse approximation problem which is resolved by rewriting the ${\ell }_0$ pseudo-norm using an auxiliary term. In the preliminary experiments with the simulated ISBI datasets the algorithm yields as good results as the state-of-the-art in high-density molecule localization algorithms.

In Proceedings of iTWIST'18, Paper-ID: 13, Marseille, France, November, 21-23, 2018

References in corpus (1)

• Sparsity Constrained Minimization via Mathematical Programming with Equilibrium Constraints