Open AccessOne-shot phase retrieval method for interferometry using a hypercolumns convolutional neural network
Author(s) -
Zhuo Zhao,
Bing Li,
Jiasheng Lu,
Xiaoqin Kang,
Xilin Hou
Publication year - 2021
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.410723
Subject(s) - computer science , phase retrieval , convolutional neural network , interferometry , phase (matter) , artificial intelligence , frame (networking) , pattern recognition (psychology) , artificial neural network , algorithm , optics , fourier transform , mathematics , mathematical analysis , telecommunications , chemistry , physics , organic chemistry
In three dimensional profilometry, phase retrieval technique plays a key role in signal processing stage. Fringe images need to be transformed into phase information to obtain the measurement result. In this paper, a new phase retrieval method based on deep learning technique is proposed for interferometry. Different from conventional multi-step phase shift methods, phase information can be extracted from only a single frame of an interferogram by this method. Here, the phase retrieval task is regarded as a regression problem and a hypercolumns convolutional neural network is constructed to solve it. Firstly, functions and each component of the network model are introduced in details; Then, four different mathematical functions are adopted to generate the training dataset; training and validation strategies are also designed subsequently; Finally, optimization processing is performed to eliminate local data defects in initial results with the help of polynomial fitting. In addition, hardware platform based on point diffraction interferometer is fabricated to support this method. Concluded from the experiment section, the proposed method possesses a desirable performance in terms of phase retrieval, denoising and time efficiency.