Shack-Hartman Detector Real-time Wavefront Processor Based on FPGA
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摘要: 夏克-哈特曼波前传感器是目前自适应光学系统中应用最为广泛的实时波前探测器。本文针对具有高分辨、高帧速、大规模子孔径数的夏克-哈特曼传感器,根据其波前处理计算量和实时性的要求,提出了一种基于现场可编程门阵列(FPGA,field-programmable gate array)的实时波前处理机结构及波前斜率计算方法。该方法利用核心处理模块重复利用的方式完成子孔径内光斑质心的计算,并通过USB3.0将处理后的质心数据实时传输给PC机。处理机以一片XILINX公司Kintex7-XC7K325T的FPGA作为处理芯片进行了设计,结果表明:该算法可对560帧/s的1020×1020图像(580 MB/s数据量),56×56子孔径哈特曼传感器,进行低延时实时光斑质心计算,提高了系统的波前处理速度和整个自适应光学系统的控制速度。Abstract: The Shack-Hartman wavefront sensor is the most widely used real-time wavefront detector in adaptive optics systems. In this study, a Shack-Hartmann sensor with high resolution, high frame rate, and a large-scale sub-aperture number is proposed. Based on the requirements of wavefront processing calculations and real-time performance, a field-programmable gate array (FPGA) is also proposed. The real-time wavefront processor structure and wavefront slope calculation method are investigated. The system employed the core processing module to reuse the method to calculate the centroid of the spot in the sub-aperture and transmitted the processed centroid data to the PC in real time through USB 3.0. The processor was designed with a XILINX Kintex7-325T FPGA processing chip. The results demonstrate that the algorithm can perform low-latency, real-time operations on 1020×1020 images and 56×56 sub-aperture Hartmann sensors at 560 frames per second. The spot centroid calculation increased the wavefront processing speed of the system and the control speed of the entire adaptive optics system.
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Key words:
- Shack-Hartmann sensor /
- real-time wavefront detector /
- FPGA
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表 1 处理能力比较
Table 1. Comparison of processing power
Processor Number of sub-apertures The data volume of the sensor Processing delay 2002 Institute of Optics and Electronics, Chinese Academy of Sciences[1] 17DSP 61 7.8MB/s 340μs 2011 Institute of Optics and Electronics, Chinese Academy of Sciences[5] FPGA 177 No data 338μs 2014 Technische Universität Ilmenau[6] FPGA 120 43.3MB/s 1050μs 2015 Institute of Optics and Electronics, Chinese Academy of Sciences[2] Computer 949 88.2MB/s 163μs 2016 Changchun Institute of Optics and Mechanics[4] GK104 GPU+CPU 1600 115.2MB/s 18μs 2018 Changchun Institute of Optics and Mechanics[7] FPGA 349 113.5MB/s 198μs 2018 Indian Institute of Astrophysics[8] FPGA 2500 No data 24-26μs System and method of this paper FPGA 3136 580MB/s μs -
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