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arxiv: 1812.05971 · v1 · pith:FF4GUA6Ynew · submitted 2018-12-14 · 📡 eess.IV · cs.IT· math.IT· math.OC

Single molecule localization by ell₂-ell₀ constrained optimization

classification 📡 eess.IV cs.ITmath.ITmath.OC
keywords localizationmoleculeconstrainedproblemsinglesparseacquisitionactivation
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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.

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