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Optical-numerical method based on a convolutional neural network for full-field subpixel displacement measurements

Authors :
Zhao Jian
Ren Qing
Chaochen Ma
Source :
Optics Express. 29:9137
Publication Year :
2021
Publisher :
Optica Publishing Group, 2021.

Abstract

The subpixel displacement estimation is an important step to calculation of the displacement between two digital images in optics and image processing. Digital image correlation (DIC) is an effective method for measuring displacement due to its high accuracy. Various DIC algorithms to compare images and to obtain displacement have been implemented. However, there are some drawbacks to DIC. It can be computationally expensive when processing a sequence of continuously deformed images. To simplify the subpixel displacement estimation and to explore a different measurement scheme, a convolutional neural network with a transfer learning based subpixel displacement measurement method (CNN-SDM) is proposed in this paper. The basic idea of the method is to compare images of an object decorated with speckle patterns before and after deformation by CNN, and thereby to achieve a coarse-to-fine subpixel displacement estimation. The proposed CNN is a classification model consisting of two convolutional neural networks in series. The results of simulated and real experiments are shown that the proposed CNN-SDM method is feasibly effective for subpixel displacement measurement due its high efficiency, robustness, simple structure and few parameters.

Details

ISSN :
10944087
Volume :
29
Database :
OpenAIRE
Journal :
Optics Express
Accession number :
edsair.doi.dedup.....244978cc217b16ae0520a9b371a69adc
Full Text :
https://doi.org/10.1364/oe.417413