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The vortex beam can improve the resolution of the interferometry by converting the continuous phase shift into the rotation angle of the interference image. This research focuses on the high-resolution recognition of rotating interference images in the vortex beam interferometry. The present paper explores deep-learning technology by establishing a residual convolutional neural network to recognize the rotation angles of interference profiles. After well trained, the proposed network model is able to achieve an image rotation recognition resolution of 9.19 milli-radians, and the corresponding displacement measurement resolution is 0.92 nm. Due to the competitive resolution, the proposed method shows great potential in precise measurements.
(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Xibo Sun,Yilin Yao,Yuanchao Geng, andYu Xie
"Deep-learning-based high-resolution recognition of rotating interference images in vortex beam interferometry", Proc. SPIE 12974, Fifth International Symposium on High Power Laser Science and Engineering (HPLSE 2023), 129740B (22 December 2023); https://doi.org/10.1117/12.3014898
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Xibo Sun, Yilin Yao, Yuanchao Geng, Yu Xie, "Deep-learning-based high-resolution recognition of rotating interference images in vortex beam interferometry," Proc. SPIE 12974, Fifth International Symposium on High Power Laser Science and Engineering (HPLSE 2023), 129740B (22 December 2023); https://doi.org/10.1117/12.3014898