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Proceedings Paper

Computational super-resolution microscopy: leveraging noise model, regularization and sparsity to achieve highest resolution
Author(s): Jian Xing; Simeng Chen; Stephen Becker; Jiun-Yann Yu; Carol Cogswell
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Paper 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.

Paper Details

Date Published: 17 February 2020
PDF: 12 pages
Proc. SPIE 11245, Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing XXVII, 112450O (17 February 2020); doi: 10.1117/12.2551542
Show Author Affiliations
Jian Xing, Univ. of Colorado Boulder (United States)
Simeng Chen, Univ. of Colorado Boulder (United States)
Stephen Becker, Univ. of Colorado Boulder (United States)
Jiun-Yann Yu, Univ. of Colorado Boulder (United States)
Carol Cogswell, Univ. of Colorado Boulder (United States)


Published in SPIE Proceedings Vol. 11245:
Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing XXVII
Thomas G. Brown; Tony Wilson; Laura Waller, Editor(s)

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