Paper
2 February 2009 Quantitative phase and amplitude imaging using Differential-Interference Contrast (DIC) microscopy
Author Affiliations +
Proceedings Volume 7246, Computational Imaging VII; 724604 (2009) https://doi.org/10.1117/12.819929
Event: IS&T/SPIE Electronic Imaging, 2009, San Jose, California, United States
Abstract
We present an extension of the development of an alternating minimization (AM) method for the computation of a specimen's complex transmittance function (magnitude and phase) from DIC images. The ability to extract both quantitative phase and amplitude information from two rotationally-diverse DIC images (i.e., acquired by rotating the sample) extends previous efforts in computational DIC microscopy that have focused on quantitative phase imaging only. Simulation results show that the inverse problem at hand is sensitive to noise as well as to the choice of the AM algorithm parameters. The AM framework allows constraints and penalties on the magnitude and phase estimates to be incorporated in a principled manner. Towards this end, Green and De Pierro's "log-cosh" regularization penalty is applied to the magnitude of differences of neighboring values of the complex-valued function of the specimen during the AM iterations. The penalty is shown to be convex in the complex space. A procedure to approximate the penalty within the iterations is presented. In addition, a methodology to pre-compute AM parameters that are optimal with respect to the convergence rate of the AM algorithm is also presented. Both extensions of the AM method are investigated with simulations.
© (2009) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Chrysanthe Preza and Joseph A. O'Sullivan "Quantitative phase and amplitude imaging using Differential-Interference Contrast (DIC) microscopy", Proc. SPIE 7246, Computational Imaging VII, 724604 (2 February 2009); https://doi.org/10.1117/12.819929
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Cited by 2 scholarly publications.
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KEYWORDS
Digital image correlation

Amplitude modulation

Microscopy

Computer simulations

Algorithm development

Inverse problems

Objectives

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