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Fringe pattern based measurement techniques are the state-of-the-art infull-field optical metrology. They are crucial both in macroscale, e.g., fringeprojection profilometry, and microscale, e.g., label-free quantitative phasemicroscopy. Accurate estimation of the local fringe orientation map cansignificantly facilitate the measurement process on various ways, e.g., fringefiltering (denoising), fringe pattern boundary padding, fringe skeletoning(contouring/following/tracking), local fringe spatial frequency (fringe period)estimation and fringe pattern phase demodulation. Considering all of that theaccurate, robust and preferably automatic estimation of local fringeorientation map is of high importance. In this paper we propose novel numericalsolution for local fringe orientation map estimation based on convolutionalneural network and deep learning called DeepOrientation. Numerical simulationsand experimental results corroborate the effectiveness of the proposedDeepOrientation comparing it with the representative of the classical approachto orientation estimation called combined plane fitting/gradient method. Theexample proving the effectiveness of DeepOrientation in fringe patternanalysis, which we present in this paper is the application of DeepOrientationfor guiding the phase demodulation process in Hilbert spiral transform. Inparticular, living HeLa cells quantitative phase imaging outcomes verify themethod as an important asset in label-free microscopy.

DeepOrientation: convolutional neural network for fringe pattern orientation map estimation