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Optical-numerical method based on a convolutional neural network for full-field subpixel displacement measurements
- 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.
- Subjects :
- Laser velocimetry
Digital image correlation
Artificial neural network
business.industry
Computer science
Optical flow
Image processing
02 engineering and technology
021001 nanoscience & nanotechnology
01 natural sciences
Convolutional neural network
Subpixel rendering
Atomic and Molecular Physics, and Optics
Displacement (vector)
010309 optics
Digital image
Speckle pattern
Optics
Computer Science::Computer Vision and Pattern Recognition
0103 physical sciences
Computer vision
Artificial intelligence
0210 nano-technology
business
Subjects
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