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Proceedings Paper

Computational microscopy: illumination coding and nonlinear optimization enables gigapixel 3D phase imaging
Author(s): Lei Tian; Laura Waller
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Paper Abstract

Microscope lenses can have either large field of view (FOV) or high resolution, not both. Computational microscopy based on illumination coding circumvents this limit by fusing images from different illumination angles using nonlinear optimization algorithms. The result is a Gigapixel-scale image having both wide FOV and high resolution. We demonstrate an experimentally robust reconstruction algorithm based on a 2nd order quasi-Newton's method, combined with a novel phase initialization scheme. To further extend the Gigapixel imaging capability to 3D, we develop a reconstruction method to process the 4D light field measurements from sequential illumination scanning. The algorithm is based on a 'multislice' forward model that incorporates both 3D phase and diffraction effects, as well as multiple forward scatterings. To solve the inverse problem, an iterative update procedure that combines both phase retrieval and 'error back-propagation' is developed. To avoid local minimum solutions, we further develop a novel physical model-based initialization technique that accounts for both the geometric-optic and 1st order phase effects. The result is robust reconstructions of Gigapixel 3D phase images having both wide FOV and super resolution in all three dimensions. Experimental results from an LED array microscope were demonstrated.

Paper Details

Date Published: 10 May 2017
PDF: 7 pages
Proc. SPIE 10219, Three-Dimensional Imaging, Visualization, and Display 2017, 102190N (10 May 2017); doi: 10.1117/12.2264063
Show Author Affiliations
Lei Tian, Boston Univ. (United States)
Laura Waller, Univ. of California, Berkeley (United States)


Published in SPIE Proceedings Vol. 10219:
Three-Dimensional Imaging, Visualization, and Display 2017
Bahram Javidi; Jung-Young Son; Osamu Matoba, Editor(s)

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