非线性图像扩散LB 模型的CUDA 算法设计与实现

doi:10.3969/j.issn.0255-8297.2014.01.014

应用科学学报 ›› 2014, Vol. 32 ›› Issue (1): 85-92.doi: 10.3969/j.issn.0255-8297.2014.01.014

非线性图像扩散LB 模型的CUDA 算法设计与实现

周明，严壮志，黄彬

上海大学通信与信息工程学院，上海200072

收稿日期:2013-01-29 修回日期:2013-03-25 出版日期:2014-01-31 发布日期:2013-03-25
作者简介:严壮志，教授，博导，研究方向：生物医学图像与信息处理，E-mail：zzyan@shu.edu.cn
基金资助:
国家自然科学基金(No.61171146)；上海市科委科技创新行动计划基金(No.11DZ1921702)资助

Design and Implementation of CUDA Algorithms Based on Nonlinear Image Diffusion LB model

ZHOU Ming, YAN Zhuang-zhi, HUANG Bin

School of Communication and Information Engineering, Shanghai University, Shanghai 200072, China

Received:2013-01-29 Revised:2013-03-25 Online:2014-01-31 Published:2013-03-25

摘要/Abstract

摘要： 为提高基于格子波尔兹曼(Lattice Boltzmann, LB) 模型图像去噪方法的性能，研究了非线性图像扩散LB 模型的CUDA 算法，即分别利用纹理内存、共享内存以及直接使用全局内存来实现非线性图像扩散LB 模型中的迁移过程. 利用合成图像和真实图像的去噪实验表明，针对非线性图像扩散LB 模型，GPU 相对CPU 的加
速比可达90 倍以上；而且加速比的提高与GPU 流处理器的数目成正比.

关键词: 图像去噪, 非线性图像扩散, LB 模型, CUDA 算法

Abstract: To improve the performance of Lattice Boltzmann (LB) method in image denoising, this paper proposes three compute unified device architecture (CUDA) algorithms to realize streaming processes of LB in nonlinear image diffusion, which are based on texture memory, shared memory, and global memory, respectively.To test effectiveness and efficiency of the GUDA algorithms, experiments were carried out with natural and composite images. The results show that GPU acceleration is 90 times faster than CPU acceleration, and the factor is proportional to the number of GPU stream processors.

Key words: image denoising, nonlinear image diffusion, LB model, CUDA algorithm

中图分类号:

TP391

周明，严壮志，黄彬. 非线性图像扩散LB 模型的CUDA 算法设计与实现[J]. 应用科学学报, 2014, 32(1): 85-92.

ZHOU Ming, YAN Zhuang-zhi, HUANG Bin. Design and Implementation of CUDA Algorithms Based on Nonlinear Image Diffusion LB model[J]. Journal of Applied Sciences, 2014, 32(1): 85-92.

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非线性图像扩散LB 模型的CUDA 算法设计与实现

Design and Implementation of CUDA Algorithms Based on Nonlinear Image Diffusion LB model

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