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

Miu Tamamitsu; Yibo Zhang; Hongda Wang; Yichen Wu; Aydogan Ozcan

doi:10.1117/12.2291179

23 February 2018 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

Miu Tamamitsu, Yibo Zhang, Hongda Wang, Yichen Wu, Aydogan Ozcan

Author Affiliations +

Proceedings Volume 10503, Quantitative Phase Imaging IV; 105030J (2018) https://doi.org/10.1117/12.2291179
Event: SPIE BiOS, 2018, San Francisco, California, United States

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.

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Miu Tamamitsu, Yibo Zhang, Hongda Wang, Yichen Wu, and Aydogan Ozcan "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", Proc. SPIE 10503, Quantitative Phase Imaging IV, 105030J (23 February 2018); https://doi.org/10.1117/12.2291179

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