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Proceedings Paper

Autofocusing in digital holography using deep learning
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Paper Abstract

In digital holography, it is critical to know the distance in order to reconstruct the multi-sectional object. This autofocusing is traditionally solved by reconstructing a stack of in-focus and out-of-focus images and using some focus metric, such as entropy or variance, to calculate the sharpness of each reconstructed image. Then the distance corresponding to the sharpest image is determined as the focal position. This method is effective but computationally demanding and time-consuming. To get an accurate estimation, one has to reconstruct many images. Sometimes after a coarse search, a refinement is needed. To overcome this problem in autofocusing, we propose to use deep learning, i.e., a convolutional neural network (CNN), to solve this problem. Autofocusing is viewed as a classification problem, in which the true distance is transferred as a label. To estimate the distance is equated to labeling a hologram correctly. To train such an algorithm, totally 1000 holograms are captured under the same environment, i.e., exposure time, incident angle, object, except the distance. There are 5 labels corresponding to 5 distances. These data are randomly split into three datasets to train, validate and test a CNN network. Experimental results show that the trained network is capable of predicting the distance without reconstructing or knowing any physical parameters about the setup. The prediction time using this method is far less than traditional autofocusing methods.

Paper Details

Date Published: 23 February 2018
PDF: 8 pages
Proc. SPIE 10499, Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing XXV, 104991V (23 February 2018);
Show Author Affiliations
Zhenbo Ren, The Univ. of Hong Kong (Hong Kong, China)
Zhimin Xu, SharpSight Ltd. (Hong Kong, China)
Edmund Y. Lam, The Univ. of Hong Kong (Hong Kong, China)

Published in SPIE Proceedings Vol. 10499:
Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing XXV
Thomas G. Brown; Carol J. Cogswell; Tony Wilson, Editor(s)

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