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Proceedings Paper

Physics-based learning for measurement diversity in 3D refractive index microscopy (Conference Presentation)

Paper Abstract

3D refractive index imaging methods usually rely on a weak-scattering approximation that does not allow for thick samples to be imaged accurately. Recent methods such as 3D Fourier ptychographic microscopy (FPM) instead use multiple-scattering models which allow for thicker objects to be imaged. In practice the illumination-side coding of 3D FPM requires redundant information and may produce inaccurate reconstructions for thick samples. Here, we propose augmenting 3D FPM with detection-side coding using a spatial light modulator (SLM) and optimize the SLM coding strategy with physics-based machine learned pupil coding designs that are optimized for 3D reconstructions. We compare our learned designs to random-, defocus-, Zernike aberrations-based pupil codes in simulated and experimental results.

Paper Details

Date Published: 9 March 2020
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Proc. SPIE 11245, Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing XXVII, 112450X (9 March 2020); doi: 10.1117/12.2543402
Show Author Affiliations
Regina Eckert, Univ. of California, Berkeley (United States)
Michael R. Kellman, Univ. of California, Berkeley (United States)
Laura Waller, Univ. of California, Berkeley (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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