27 May 2017 Investigation of sparsity metrics for autofocusing in digital holographic microscopy
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Abstract
Digital holographic microscopy (DHM) is an optoelectronic technique that is made up of two parts: (i) the recording of the interference pattern of the diffraction pattern of an object and a known reference wavefield using a digital camera and (ii) the numerical reconstruction of the complex object wavefield using the recorded interferogram and a distance parameter as input. The latter is based on the simulation of optical propagation from the camera plane to a plane at any arbitrary distance from the camera. A key advantage of DHM over conventional microscopy is that both the phase and intensity information of the object can be recovered at any distance, using only one capture, and this facilitates the recording of scenes that may change dynamically and that may otherwise go in and out of focus. Autofocusing using traditional microscopy requires mechanical movement of the translation stage or the microscope objective, and multiple image captures that are then compared using some metric. Autofocusing in DHM is similar, except that the sequence of intensity images, to which the metric is applied, is generated numerically from a single capture. We recently investigated the application of a number of sparsity metrics for DHM autofocusing and in this paper we extend this work to include more such metrics, and apply them over a greater range of biological diatom cells and magnification/numerical apertures. We demonstrate for the first time that these metrics may be grouped together according to matching behavior following high pass filtering.
© 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)
Xin Fan, Xin Fan, John J. Healy, John J. Healy, Bryan M. Hennelly, Bryan M. Hennelly, } "Investigation of sparsity metrics for autofocusing in digital holographic microscopy," Optical Engineering 56(5), 053112 (27 May 2017). https://doi.org/10.1117/1.OE.56.5.053112 . Submission: Received: 28 October 2016; Accepted: 1 May 2017
Received: 28 October 2016; Accepted: 1 May 2017; Published: 27 May 2017
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