  In many optical metrology techniques, fringe pattern analysis is the central
algorithm for recovering the underlying phase distribution from the recorded
fringe patterns. Despite extensive research efforts for decades, how to extract
the desired phase information, with the highest possible accuracy, from the
minimum number of fringe patterns remains one of the most challenging open
problems. Inspired by recent successes of deep learning techniques for computer
vision and other applications, here, we demonstrate for the first time, to our
knowledge, that the deep neural networks can be trained to perform fringe
analysis, which substantially enhances the accuracy of phase demodulation from
a single fringe pattern. The effectiveness of the proposed method is
experimentally verified using carrier fringe patterns under the scenario of
fringe projection profilometry. Experimental results demonstrate its superior
performance in terms of high accuracy and edge-preserving over two
representative single-frame techniques: Fourier transform profilometry and
Windowed Fourier profilometry.
