吉林大学学报(信息科学版) ›› 2016, Vol. 34 ›› Issue (1): 104-110.

• 论文 • 上一篇    下一篇

引导滤波算法的CUDA 加速实现

王新磊, 何凯, 王晓文   

  1. 天津大学电子信息工程学院, 天津300072
  • 收稿日期:2015-05-26 出版日期:2016-01-25 发布日期:2016-05-10
  • 作者简介:王新磊(1988—), 男, 山东淄博人, 天津大学硕士研究生, 主要从事数字图像与视频处理研究, (Tel)86-15620787499 (E-mail)yt-wangxinlei@163. com; 通讯作者: 何凯(1972—), 男, 沈阳人, 天津大学副教授, 硕士生导师, 主要从事数字 图像处理研究, (Tel)86-15510810452(E-mail)hekai626@163. com。
  • 基金资助:

    国家自然科学基金资助项目(61271326)

Speed-up Implementation of Guided Filtering Approach Based on CUDA

WANG Xinlei, HE Kai, WANG Xiaowen   

  1. School of Electronic Information Engineering, Tianjin University, Tianjin 300072, China
  • Received:2015-05-26 Online:2016-01-25 Published:2016-05-10

摘要:

针对引导滤波算法运算速度慢、无法实时处理的问题, 提出基于统一计算设备架构(CUDA: Compute Unified Device Architecture)实现引导滤波算法的加速。利用CUDA 并行编程实现图像邻域窗口像素值求和,进而获得图像邻域均值; 通过利用寄存器和纹理存储器, 同时优化算法步骤, 获得引导滤波关键参数, 进而实现对算法的整体优化。实验结果表明, 与基于CPU 实现引导滤波算法相比, 基于CUDA 并行处理可在很大程度上提高运算速度, 基本达到了实时处理的要求。

关键词: 引导滤波, 统一计算设备架构, 并行计算, 优化技术

Abstract:

For the shortcoming of guided filtering, such as slow operational speed and non-real time processing,the algorithm is speeded up based on CUDA(Compute Unified Device Architecture). In the proposed method,the sum of neighbor pixels-value is calculated based on CUDA parallel programming, and the mean value is calculated. The key parameters of guided filtering are obtained by taking advantage of texture memory and registers and algorithm optimizing. The whole optimum of approach is achieved. Experimental results show that, compared with CPU-based guided filtering algorithm, the proposed CUDA-based algorithm is greatly speeded up and basically meets the requirement of real-time processing.

Key words: guided filtering, compute unified device architecture ( CUDA ), parallel processing, optimization technique

中图分类号: 

  • TP391. 41