9 research outputs found
DeepOrientation: convolutional neural network for fringe pattern orientation map estimation
Fringe pattern based measurement techniques are the state-of-the-art in
full-field optical metrology. They are crucial both in macroscale, e.g., fringe
projection profilometry, and microscale, e.g., label-free quantitative phase
microscopy. Accurate estimation of the local fringe orientation map can
significantly facilitate the measurement process on various ways, e.g., fringe
filtering (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 the
accurate, robust and preferably automatic estimation of local fringe
orientation map is of high importance. In this paper we propose novel numerical
solution for local fringe orientation map estimation based on convolutional
neural network and deep learning called DeepOrientation. Numerical simulations
and experimental results corroborate the effectiveness of the proposed
DeepOrientation comparing it with the representative of the classical approach
to orientation estimation called combined plane fitting/gradient method. The
example proving the effectiveness of DeepOrientation in fringe pattern
analysis, which we present in this paper is the application of DeepOrientation
for guiding the phase demodulation process in Hilbert spiral transform. In
particular, living HeLa cells quantitative phase imaging outcomes verify the
method as an important asset in label-free microscopy
Single-shot fringe pattern phase retrieval using improved period-guided bidimensional empirical mode decomposition and Hilbert transform
Fringe pattern analysis is the central aspect of numerous optical measurement methods, e.g., interferometry, fringe projection, digital holography, quantitative phase microscopy. Experimental fringe patterns always contain significant features originating from fluctuating environment, optical system and illumination quality, and the sample itself that severely affect analysis outcome. Before the stage of phase retrieval (information decoding) interferogram needs proper filtering, which minimizes the impact of mentioned issues. In this paper we propose fully automatic and adaptive fringe pattern pre-processing technique - improved period guided bidimensional empirical mode decomposition algorithm (iPGBEMD). It is based on our previous work about PGBEMD which eliminated the mode-mixing phenomenon and made the empirical mode decomposition fully adaptive. In present work we overcame key problems of original PGBEMD – we have considerably increased algorithm’s application range and shortened computation time several-fold. We proposed three solutions to the problem of erroneous decomposition for very low fringe amplitude images, which limited original PGBEMD significantly and we have chosen the best one among them after comprehensive analysis. Several acceleration methods were also proposed and merged to ensure the best results. We combined our improved pre-processing algorithm with the Hilbert Spiral Transform to receive complete, consistent, and versatile fringe pattern analysis path. Quality and effectiveness evaluation, in comparison with selected reference methods, is provided using numerical simulations and experimental fringe data