title: Denoising by coupled partial differential equations and extracting phase by backpropagation neural networks for electronic speckle pattern interferometry.
abstract: We extend and refine previous work [Appl. Opt. 46, 2907 (2007)]. Combining the coupled nonlinear partial differential equations (PDEs) denoising model with the ordinary differential equations enhancement method, we propose the new denoising and enhancing model for electronic speckle pattern interferometry (ESPI) fringe patterns. Meanwhile, we propose the backpropagation neural networks (BPNN) method to obtain unwrapped phase values based on a skeleton map instead of traditional interpolations. We test the introduced methods on the computer-simulated speckle ESPI fringe patterns and experimentally obtained fringe pattern, respectively. The experimental results show that the coupled nonlinear PDEs denoising model is capable of effectively removing noise, and the unwrapped phase values obtained by the BPNN method are much more accurate than those obtained by the well-known traditional interpolation. In addition, the accuracy of the BPNN method is adjustable by changing the parameters of networks such as the number of neurons.
authors.affiliation: Department of Applied Physics, University of Tianjin, China. tangchen@tju.edu.cn
authors.forename: Chen
authors.initials: C
authors.lastname: Tang
authors.forename: Wenjing
authors.initials: W
authors.lastname: Lu
authors.forename: Song
authors.initials: S
authors.lastname: Chen
authors.forename: Zhen
authors.initials: Z
authors.lastname: Zhang
authors.forename: Botao
authors.initials: B
authors.lastname: Li
authors.forename: Wenping
authors.initials: W
authors.lastname: Wang
authors.forename: Lin
authors.initials: L
authors.lastname: Han
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D004581:Electronics; D004583:Electrons; D015233:Models, Statistical; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D055095:Optics and Photonics; D015203:Reproducibility of Results; D012984:Software