计算机科学 ›› 2010, Vol. 37 ›› Issue (1): 265-267.

• 图形图像及体系结构 • 上一篇    下一篇

基于图形处理器的边缘检测算法

张楠,王建立,王鸣浩   

  1. (中国科学院长春光学精密机械与物理研究所 长春130033);(中国科学院研究生院 北京100039)
  • 出版日期:2018-12-01 发布日期:2018-12-01
  • 基金资助:
    本文受863国家重点基金项目(2007AA703104)资助。

Edge Detection Based on GPU

ZHANG Nan,WANG Jian-li,WANG Ming-hao   

  • Online:2018-12-01 Published:2018-12-01

摘要: 边缘检测是一种高度并行的算法,计算量较大,传统的CPU处理难以满足实时要求。针对图像边缘检测问题的计算密集性,在分析常用边缘检测算法的基础上,利用CUDA(Compute Unified Device Architecture,计算统一设备架构)软硬件体系架构,提出了图像边缘检测的GPU(Graphics Processing Unit,图形处理器)实现方案。首先介绍GPU高强度并行运算的体系结构基础,并将Robots和Sobcl这两个具有代表性的图像边缘检测算法移植到CPU,然后利用当前同等价格的CPU和GPU进行对比实验,利用多幅不同分辫率图像作为测试数据,对比CPU和GPU方案的计算效率。实验结果表明,与相同算法的CPU实现相比,其GPU实现获得了相同的处理效果,并将计算效率最高提升到了17倍以上,以此证明GPU在数字图像处理的实际应用中大有潜力。

关键词: 图像处理,边缘检测,图形处理器,计算统一设备架构

Abstract: Edge detection is a highly parallel algorithm with great computation. It is difficult to increase the speed of the algorithm by CPU to satisfy the real time application. Aiming at the computcintensive character of image edge deteclion, this paper analyzed some methods of edge detection based on CPU, using the programmer friendly CUDA framework, and proposed a method based on GPU, to realize the image edge detection. The efficient architecture of GPU was introduced firstly. Then, two representative image edge detection algorithms, Roberts and Sobel, were implemented on GPU. At last, using the same market price level CPU and GPU as hardware platform, and using various resolution images as test data, compared the computational efficiency of GPU and CPU. Numerical experiments show that the speed of the algorithm can be improved by up to more than 17 times compared with CPU-based implementations,with the same processing results. It proves that the GPU is practical for some applications of image processing.

Key words: Image processing, Edge detection, GPU, CUDA

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