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Canny算法的GPU并行加速

Parallel acceleration of Canny algorithm based on GPU

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摘要

Canny算法在PC机上的执行速度较慢, 这极大地限制了其实用性。本文在前人的研究基础上对算法进行更深的优化和改进。首先在VS2012开发环境下利用数字图像处理技术对原算法进行原理上的改进, 再利用GPU流处理器数量众多的优势以及强大的多线程并发执行能力对Canny算法进行并行加速。在500 pixel×500 pixel的图片上, 对本文算法和原Canny算法进行了实验验证。实验结果表明, 在4 096 pixel×4 096 pixel大小的图片上采用本文的GPU移植算法处理后, 执行速度从80 ms降到了6 ms以内。在不影响边缘检测效果的前提下极大地提高了算法的实用性。

Abstract

Due to the slow execution speed of Canny algorithm in PC, the practicality of this algorithm is greatly restricted. Based on the previous studies, we further optimizes and improves the algorithm. First of all, we use the digital image processing technology to improve the original algorithm under the development environment of VS2012, and then accelerate the Canny algorithm by taking advantage of the large number of GPU stream processors and powerful multithreaded concurrent execution capability. Experiments were made on the improved algorithm and the original Canny algorithm. Experimental results show that in the 4 096×4 096 pixel-size images, the GPU migration algorithm presented in this paper can reduce the execution speed from 80 ms to less than 6 ms. Through this improvement, it can greatly improve the practicability of the algorithm without affecting the edge detection effect.

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中图分类号:TP751.1

DOI:10.3788/co.20171006.0737

所属栏目:信息光学

基金项目:国家高技术研究发展计划(863计划)资助项目(No.863-2-5-1-13B)

收稿日期:2017-09-11

修改稿日期:2017-11-13

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张 帆:中国科学院 长春光学精密机械与物理研究所, 吉林 长春 130033中国科学院大学, 北京 100049
韩树奎:东北电力设计研究院, 吉林 长春 130021
张立国:中国科学院 长春光学精密机械与物理研究所, 吉林 长春 130033
王文胜:中国科学院 长春光学精密机械与物理研究所, 吉林 长春 130033中国科学院大学, 北京 100049

联系人作者:张帆(zhangfan_6284@126.com)

备注:张 帆(1992—), 男, 河南周口人, 硕士研究生, 主要从事数字图像处理、GPU并行加速、机器视觉等方面的研究。

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引用该论文

ZHANG Fan,HAN Shu-kui,ZHANG Li-guo,WANG Wen-sheng. Parallel acceleration of Canny algorithm based on GPU[J]. Chinese Optics, 2017, 10(6): 737-743

张 帆,韩树奎,张立国,王文胜. Canny算法的GPU并行加速[J]. 中国光学, 2017, 10(6): 737-743

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