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Cross-Correlation Algorithm Based on Speeded-Up Robust Features Parallel Acceleration for Shack–Hartmann Wavefront Sensing

Year: 2024

Type: article

Abstract: A cross-correlation algorithm to obtain the sub-aperture shifts that occur is a crucial aspect of scene-based SHWS (Shack–Hartmann wavefront sensing). However, when the sub-image is partially absent within the atmosphere, the traditional cross-correlation algorithm can easily obtain the wrong shift results. To overcome this drawback, we propose an algorithm based on SURFs (speeded-up-robust features) matching. In addition, to meet the speed required by wavefront sensing, CUDA parallel optimization of SURF matching is carried out using a GPU thread execution model and a programming model. The results show that the shift error can be reduced by more than two times, and the parallel algorithm can achieve nearly ten times the acceleration ratio.

Source: Photonics

DOI: 10.3390/photonics11090844

Authors: Linxiong Wen, Xiaohan Mei, Yi Tan, Zhiyun Zhang, Fangfang Chai, Jiayao Wu, Shuai Wang, Ping Yang

Institutions: Chinese Academy of Sciences, Institute of Optics and Electronics, Chinese Academy of Sciences, University of Chinese Academy of Sciences

Language: English

Topic: Adaptive optics and wavefront sensing

Subfield: Atomic and Molecular Physics, and Optics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,728

OpenAlex ID: W4402273026

FWCI: 0.159

Cites: 18

Cited by: 1

Related to: 10