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Proceedings Paper

A method for single image phase unwrapping based on generative adversarial networks
Author(s): Cong Li; Yong Tian; Jingdong Tian
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Paper Abstract

Phase unwrapping technology plays an important role in phase measurement profilometry. The unwrapping results directly affect the measurement accuracy. With the development of deep learning theory, it is opening a new direction to phase unwrapping algorithm. In this paper, a new neural network model based on an improved generation adversarial network (iGAN) is proposed for phase unwrapping. Compared with traditional methods, it can effectively suppress the influence of noise such as shadows, and does not need any referenced grating information. In addition, it can realize the phase unwrapping with a single image. Specifically, the algorithm is verified by the three-dimensional reconstruction with structured light based on the simulation data. The results indicate that the proposed method can successfully unwrap the phase via a single image. It also can well suppress the influence of frequency and shadows.

Paper Details

Date Published: 14 August 2019
PDF: 7 pages
Proc. SPIE 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019), 1117911 (14 August 2019); doi: 10.1117/12.2540155
Show Author Affiliations
Cong Li, Shenzhen Univ. (China)
Yong Tian, Shenzhen Univ. (China)
Jingdong Tian, Shenzhen Univ. (China)

Published in SPIE Proceedings Vol. 11179:
Eleventh International Conference on Digital Image Processing (ICDIP 2019)
Jenq-Neng Hwang; Xudong Jiang, Editor(s)

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