Computational super-resolution microscopy: leveraging noise model, regularization and sparsity to achieve highest resolution

Jian Xing; Simeng Chen; Stephen Becker; Jiun-Yann Yu; Carol Cogswell

doi:10.1117/12.2551542

17 February 2020 Computational super-resolution microscopy: leveraging noise model, regularization and sparsity to achieve highest resolution

Jian Xing, Simeng Chen, Stephen Becker, Jiun-Yann Yu, Carol Cogswell

Author Affiliations +

Proceedings Volume 11245, Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing XXVII; 112450O (2020) https://doi.org/10.1117/12.2551542
Event: SPIE BiOS, 2020, San Francisco, California, United States

Abstract

We report progress in algorithm development for a computation-based super-resolution microscopy technique. Building upon previous results, we examine our recently implemented microscope system and construct alter- native processing algorithms. Based on numerical simulations results, we evaluate the performance of each algorithm and determine the one most suitable for our super-resolution microscope.

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Jian Xing, Simeng Chen, Stephen Becker, Jiun-Yann Yu, and Carol Cogswell "Computational super-resolution microscopy: leveraging noise model, regularization and sparsity to achieve highest resolution", Proc. SPIE 11245, Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing XXVII, 112450O (17 February 2020); https://doi.org/10.1117/12.2551542

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