Non-uniform sampling and Gaussian process regression in transport of intensity phase imaging

Gaussian process (GP) regression is a nonparametric regression method that can be used to predict continuous quantities. Here, we show that the same technique can be applied to a class of phase imaging techniques based on measurements of intensity at multiple propagation distances, i.e. the transpor...

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Bibliographic Details
Published in2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) pp. 7784 - 7788
Main Authors Zhong Jingshan, Claus, Rene A., Dauwels, Justin, Lei Tian, Waller, Laura
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2014
Subjects
Equations
Gaussian noise
Gaussian process
Gaussian processes
Imaging
Optics
Phase imaging
Phase measurement
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Summary:Gaussian process (GP) regression is a nonparametric regression method that can be used to predict continuous quantities. Here, we show that the same technique can be applied to a class of phase imaging techniques based on measurements of intensity at multiple propagation distances, i.e. the transport of intensity equation (TIE). In this paper, we demonstrate how to apply GP regression to estimate the first intensity derivative along the direction of propagation and incorporate non-uniform propagation distance sampling. The low-frequency artifacts that often occur in phase recovery using traditional methods can be significantly suppressed by the proposed GP TIE method. The method is shown to be stable with moderate amounts of Gaussian noise. We validate the method experimentally by recovering the phase of human cheek cells in a bright field microscope and show better performance as compared to other TIE reconstruction methods.
ISSN:1520-6149
2379-190X
DOI:10.1109/ICASSP.2014.6855115