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Article Dans Une Revue IEEE Transactions on Computational Imaging Année : 2019

Computational Super-Sectioning for Single-Slice Structured-Illumination Microscopy

Résumé

While structured-illumination microscopy (SIM) is inherently a three-dimensional (3-D) technique, many biological questions can be addressed from the acquisition of a single focal plane with high lateral resolution. Unfortunately, the single-slice reconstruction of thick samples suffers from defocusing. In this paper, however, we take advantage of a 3-D model of the acquisition system to derive a reconstruction method out of a single two-dimensional (2-D) SIM measurement. It enables the estimation of the out-of-focus signal and improves the quality of the reconstruction, without the need of acquiring additional slices. The proposed algorithm relies on a specific formulation of the optimization problem together with the derivation of computationally efficient proximal operators. These developments allow us to deploy an efficient inner-loop-free alternating-direction method of multipliers (ADMM), with guaranteed convergence.

Dates et versions

hal-02428166 , version 1 (05-01-2020)

Identifiants

Citer

Emmanuel Soubies, Michael Unser. Computational Super-Sectioning for Single-Slice Structured-Illumination Microscopy. IEEE Transactions on Computational Imaging, 2019, 5 (2), pp.240-250. ⟨10.1109/TCI.2018.2887136⟩. ⟨hal-02428166⟩
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