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Proceedings Paper

A robust holographic autofocusing criterion based on edge sparsity: comparison of Gini index and Tamura coefficient for holographic autofocusing based on the edge sparsity of the complex optical wavefront
Author(s): Miu Tamamitsu; Yibo Zhang; Hongda Wang; Yichen Wu; Aydogan Ozcan
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Paper Abstract

The Sparsity of the Gradient (SoG) is a robust autofocusing criterion for holography, where the gradient modulus of the complex refocused hologram is calculated, on which a sparsity metric is applied. Here, we compare two different choices of sparsity metrics used in SoG, specifically, the Gini index (GI) and the Tamura coefficient (TC), for holographic autofocusing on dense/connected or sparse samples. We provide a theoretical analysis predicting that for uniformly distributed image data, TC and GI exhibit similar behavior, while for naturally sparse images containing few high-valued signal entries and many low-valued noisy background pixels, TC is more sensitive to distribution changes in the signal and more resistive to background noise. These predictions are also confirmed by experimental results using SoG-based holographic autofocusing on dense and connected samples (such as stained breast tissue sections) as well as highly sparse samples (such as isolated Giardia lamblia cysts). Through these experiments, we found that ToG and GoG offer almost identical autofocusing performance on dense and connected samples, whereas for naturally sparse samples, GoG should be calculated on a relatively small region of interest (ROI) closely surrounding the object, while ToG offers more flexibility in choosing a larger ROI containing more background pixels.

Paper Details

Date Published: 23 February 2018
PDF: 10 pages
Proc. SPIE 10503, Quantitative Phase Imaging IV, 105030J (23 February 2018); doi: 10.1117/12.2291179
Show Author Affiliations
Miu Tamamitsu, Univ. of California, Los Angeles (United States)
Yibo Zhang, Univ. of California, Los Angeles (United States)
Hongda Wang, Univ. of California, Los Angeles (United States)
Yichen Wu, Univ. of California, Los Angeles (United States)
Aydogan Ozcan, Univ. of California, Los Angeles (United States)


Published in SPIE Proceedings Vol. 10503:
Quantitative Phase Imaging IV
Gabriel Popescu; YongKeun Park, Editor(s)

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