基于OpenCL加速的平行Canny边缘检测算法
Yupu Song1, Cailin Li2, Shiyang Xiao3
1College of Computer Engineering, Shangqiu Polytechnic, Shangqiu, China.
PloS one
|January 5, 2024
概括
一种新的细粒度平行Canny边缘检测方法增强了实时图像处理. 这种OpenCL加速算法 (OCL_Canny) 在CPU和CUDA实现上提供了显著的加快速度,证明了对大图像数据的有效性和便携性.
科学领域:
- 计算机视觉 计算机视觉
- 图像处理 图像处理
- 并行计算是一种平行计算.
背景情况:
- 巧妙的边缘检测涉及复杂的计算,具有挑战性的实时处理.
- 传统的硬件加速 (DSP,FPGA) 有长开发周期和调试困难等缺点.
- 现有的CUDA平台缺乏跨平台兼容性.
研究的目的:
- 为了提高效率,开发一种细粒度平行卡尼边缘检测方法.
- 为了实现CPU-GPU协作并行性.
- 为了解决现有的坎尼边缘检测加速技术的局限性.
主要方法:
- 优化了任务分区,以实现细粒度并行性.
- 改进了矢量内存访问模式.
- 为高效的并行执行提供NDRange优化.
- 实现CPU-GPU协作并行处理.
- 在多核CPU和CUDA架构上设计并行Canny边缘检测.
主要成果:
- OpenCL 加速的 Canny 边缘检测算法 (OCL_Canny) 在 7452x8024 分辨率下实现了比 CPU 串行算法的 20.68 倍的加快速度.
- 与CPU多线程算法相比,OCL_Canny在3500x3500分辨率下显示了3.96倍的加速度.
- 观察到OCL_Canny与基于CUDA的算法在1024x1024分辨率下加速了1.21倍.
结论:
- 拟议的并行Canny边缘检测算法证明了显著的有效性和性能可移植性.
- OCL_Canny为加速图像大数据处理提供了一个可行的解决方案.
- 该研究为实时应用中的快速图像分析提供了有价值的参考.
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