GPU加速电子剪切散斑干涉图像处理

邵珩; 周勇; 祁俊峰; 聂中原

doi:10.3788/YJYXS20193410.1021

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GPU加速电子剪切散斑干涉图像处理

图像处理 | 更新时间：2020-08-12

- GPU加速电子剪切散斑干涉图像处理
- GPU accelerated image processing for electronic speckle pattern shearing interferometry
- 液晶与显示 2019年34卷第10期页码：1021-1029
- 作者机构：
  
  北京卫星制造厂有限公司, 北京 100094
- 作者简介：
  
  [ "邵珩(1989—), 男, 湖北鄂州人, 博士, 工程师, 2017年于清华大学获得博士学位, 主要从事激光干涉测量及图像处理等方面的研究。E-mail:shao_heng@126.com" ]
- 基金信息：
  
  国家重点研发计划重大科学仪器设备开发重点专项(2016YFF0101800);Key Projects for the Development of Major Scientific Instruments and Equipment of National Key R & D Program(2016YFF0101800)
- DOI：10.3788/YJYXS20193410.1021
  中图分类号： TP312
- 收稿日期：2019-04-22，
  
  录用日期：2019-6-10，
  
  纸质出版日期：2019-10-05
- 稿件说明：
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邵珩, 周勇, 祁俊峰, 等. GPU加速电子剪切散斑干涉图像处理[J]. 液晶与显示, 2019,34(10):1021-1029.

Heng SHAO, Yong ZHOU, Jun-feng QI, et al. GPU accelerated image processing for electronic speckle pattern shearing interferometry[J]. Chinese journal of liquid crystals and displays, 2019, 34(10): 1021-1029.
邵珩, 周勇, 祁俊峰, 等. GPU加速电子剪切散斑干涉图像处理[J]. 液晶与显示, 2019,34(10):1021-1029. DOI： 10.3788/YJYXS20193410.1021.

Heng SHAO, Yong ZHOU, Jun-feng QI, et al. GPU accelerated image processing for electronic speckle pattern shearing interferometry[J]. Chinese journal of liquid crystals and displays, 2019, 34(10): 1021-1029. DOI： 10.3788/YJYXS20193410.1021.

摘要

电子剪切散斑干涉是在电子散斑干涉基础上发展起来的一种测量位移导数的新方法，尤其适合于无损检测实时测量。由于噪声干扰，测量得到的相位差图有大量噪点需要滤波去除。滤波处理计算量大，采用CPU计算需要很长时间完成。为缩短滤波时间，实现实时滤波处理，对四步相移法和Butterworth低通滤波、正余弦均值滤波和复数均值滤波算法，利用GPU并行计算能力强和存储带宽高的特性，开发了基于GPU加速计算的解相位差和滤波算法。对比基于CPU的解相位差和滤波计算，GPU加速计算将图像总体处理时间约1.9 s（i7 6500U）降低到了239~366 ms（GeForce 940MX）或86~116 ms（Quadro P3000），且正余弦均值滤波和复数均值滤波算法均可以通过降低滤波次数缩短滤波时间，从而保证在性能较弱的GPU上也能将计算时间压缩到0.2 s内，满足实时图像处理的需求。

Abstract

Electronic speckle pattern shearing interferometry (ESPSI) is a new method for measuring displacement derivatives based on electronic speckle pattern interferometry. It is especially suitable for real-time measurement of nondestructive testing. The acquired phase difference map in the ESPSI is usually seriously affected by noise. Thus filtering processing is needed to get a good phase difference map. Filtering by CPU takes too long time for real-time processing. To take full advantage of GPU's strong parallel computing capability and high storage bandwidth

GPU-accelerated phase difference calculation and filtering algorithm based on four-step phase-shift method and Butterworth low-pass filter

sine-cosine mean filter and complex mean filter methods were developed to accelerate image processing. GPU-accelerate processing shortened the processing time from about 1.9 s (i7 6500U) to 239~366 ms (GeForce 940MX) or 86~116 ms (Quadro P3000). The time cost by sine-cosine mean filter and complex mean filter methods can be further reduced to less than 0.2 s by filtering fewer times to meet the needs of real-time ESPSI measurement when the performance of the GPU is weak.

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references

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