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Communication Dans Un Congrès Année : 2021

A continuous, non-convex & sparse super-resolution approach for fluorescence microscopy data with Poisson noise

Résumé

We propose a non-convex sparsity-promoting variational model for the problem of super-resolution in Single Molecule Localization Microscopy (SMLM). Namely, we study a continuous non-convex relaxation of a non-continuous and non-convex variational model where a weighted-ℓ 2 data fidelity modeling signal-dependent Poisson noise is combined with an ℓ 0 -regularization to promote signal sparsity. The proposed relaxation is obtained by adapting the Continuous Exact ℓ 0 (CEL0) relaxation of the analogous ℓ 2 − ℓ 0 problem with Gaussian noise to the Poisson scenario, which is more realistic in fluorescence microscopy applications. The associated optimization problem is then solved by an iterative reweighted ℓ 1 (IRL1) algorithm. The weighted- ℓ 2 data fidelity leads to a challenging estimation of the algorithmic parameters for which efficient computation strategies are detailed. To validate our approach, we report qualitative and quantitative localization results for a simulated dataset, showing that the proposed weighted-CEL0 (WCEL0) model is well suited and capable to deal with Poisson measurements with high accuracy and precision.
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hal-03696272 , version 1 (15-06-2022)

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Marta Lazzaretti, Luca Calatroni, Claudio Estatico. A continuous, non-convex & sparse super-resolution approach for fluorescence microscopy data with Poisson noise. ICCSA 2021 - 21st International Conference on Computational Science and Its Applications, Sep 2021, Cagliari, Italy. pp.80-86, ⟨10.1109/ICCSA54496.2021.00021⟩. ⟨hal-03696272⟩
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