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Proceedings Paper

Computational imaging and reconstruction in digital holographic microscopy
Author(s): Edmund Lam
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Paper Abstract

Imaging systems are foundational to our observation and understanding of the world around us, and biological microscopy is our window to the microscopic world of living things. Ideally, we wish to capture all the spatial, directional, spectral, even statistical information about a specimen with infinite precision; practically, the optics and detector impose significant constraints, forcing us to choose among accepting various tradeoffs depending on the specific applications. In recent years, computational algorithms are effective in pushing these limitations. Specifically, our focus is on holographic microscopy, where the axial information is encoded in the digital holograms. By recording the interferometric patterns created by the interaction of a reference light source and an object, we can achieve volumetric imaging; equivalently, we can reconstruct individual sections of the 3D object computationally. In this work, we will overview two types of computational advances for digital holographic microscopy. First is the development of computational techniques that aim to reduce data capture and increase spatial resolution. This is possible often with appropriate image model, such as sparsity, which becomes part of the constraints in the image reconstruction process. Second relates to the recent popularity of machine learning techniques in many applications of computer vision. We will discuss how such data-driven approach to digital holography is possible, and can be effective tools among different holographic image reconstruction algorithms.

Paper Details

Date Published: 24 April 2018
PDF: 3 pages
Proc. SPIE 10711, Biomedical Imaging and Sensing Conference, 1071104 (24 April 2018); doi: 10.1117/12.2315322
Show Author Affiliations
Edmund Lam, The Univ. of Hong Kong (Hong Kong, China)

Published in SPIE Proceedings Vol. 10711:
Biomedical Imaging and Sensing Conference
Toyohiko Yatagai; Yoshihisa Aizu; Osamu Matoba; Yasuhiro Awatsuji; Yuan Luo, Editor(s)

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