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Deep learning-based super-resolution images for synchronous measurement of temperature and deformation at elevated temperature
- Source :
- Optik. 226:165764
- Publication Year :
- 2021
- Publisher :
- Elsevier BV, 2021.
-
Abstract
- Measuring temperature and deformation at elevated temperature has recently been a major concern for the engineering tests and the quality improvement of captured images is critical to it. Here, we propose a simple, high-precision and easy-to-implement technique combing the image capturing and processing methods to obtain the high-resolution images as well as the corresponding temperature and deformation fields. The super-resolution convolutional neural network (SRCNN) algorithm is used for the super-resolution reconstruction, while the deformation field is calculated by the DIC method and the temperature field is synchronous obtained by the improved two-color method. Flame heating experiment of the SiC material validated the applicability and the improvement of the proposed method, showing the improvement in image quality (with PSNR increased 16.7 %) and calculation accuracy of temperature and deformation (with error decreased 3.57 %). Meanwhile, the phenomenon of "feature point drift" is pointed out and the error of deformation measurement for high-resolution and low-resolution is analyzed. Furthermore, the sub-pixel edge detection can be achieved by the combination of the proposed method and the edge detection method, which shows a potential value in detecting of surface defects or cracks.
- Subjects :
- Materials science
Field (physics)
Image quality
Feature (computer vision)
Acoustics
Point (geometry)
Electrical and Electronic Engineering
Deformation (meteorology)
Temperature measurement
Convolutional neural network
Atomic and Molecular Physics, and Optics
Edge detection
Electronic, Optical and Magnetic Materials
Subjects
Details
- ISSN :
- 00304026
- Volume :
- 226
- Database :
- OpenAIRE
- Journal :
- Optik
- Accession number :
- edsair.doi...........cbc11f8e8eb007adbb43cbd1856c6cf4