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Applied Optics

Applied Optics

APPLICATIONS-CENTERED RESEARCH IN OPTICS

  • Editor: Joseph N. Mait
  • Vol. 53, Iss. 24 — Aug. 20, 2014
  • pp: 5439–5447

Phase discontinuity predictions using a machine-learning trained kernel

Firas Sawaf and Roger M. Groves  »View Author Affiliations


Applied Optics, Vol. 53, Issue 24, pp. 5439-5447 (2014)
http://dx.doi.org/10.1364/AO.53.005439


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Abstract

Phase unwrapping is one of the key steps of interferogram analysis, and its accuracy relies primarily on the correct identification of phase discontinuities. This can be especially challenging for inherently noisy phase fields, such as those produced through shearography and other speckle-based interferometry techniques. We showed in a recent work how a relatively small 10×10 pixel kernel was trained, through machine learning methods, for predicting the locations of phase discontinuities within noisy wrapped phase maps. We describe here how this kernel can be applied in a sliding-window fashion, such that each pixel undergoes 100 phase-discontinuity examinations—one test for each of its possible positions relative to its neighbors within the kernel’s extent. We explore how the resulting predictions can be accumulated, and aggregated through a voting system, and demonstrate that the reliability of this method outperforms processing the image by segmenting it into more conventional 10×10 nonoverlapping tiles. When used in this way, we demonstrate that our 10×10 pixel kernel is large enough for effective processing of full-field interferograms. Avoiding, thus, the need for substantially more formidable computational resources which otherwise would have been necessary for training a kernel of a significantly larger size.

© 2014 Optical Society of America

OCIS Codes
(120.2650) Instrumentation, measurement, and metrology : Fringe analysis
(120.4290) Instrumentation, measurement, and metrology : Nondestructive testing
(100.4996) Image processing : Pattern recognition, neural networks
(100.5088) Image processing : Phase unwrapping
(120.6165) Instrumentation, measurement, and metrology : Speckle interferometry, metrology

ToC Category:
Instrumentation, Measurement, and Metrology

History
Original Manuscript: April 28, 2014
Revised Manuscript: June 26, 2014
Manuscript Accepted: July 17, 2014
Published: August 15, 2014

Citation
Firas Sawaf and Roger M. Groves, "Phase discontinuity predictions using a machine-learning trained kernel," Appl. Opt. 53, 5439-5447 (2014)
http://www.opticsinfobase.org/ao/abstract.cfm?URI=ao-53-24-5439

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