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

E. Soubies, M. Unser

IEEE Transactions on Computational Imaging, vol. 5, no. 2, pp. 240-250, June 2019.

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.

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AUTHOR="Soubies, E. and Unser, M.",
TITLE="Computational Super-Sectioning for Single-Slice
        Structured-Illumination Microscopy",
JOURNAL="{IEEE} Transactions on Computational Imaging",
YEAR="2019",
volume="5",
number="2",
pages="240--250",
month="June",
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