摘要
压缩感知(Compressed Sensing,CS)的信号重构部分需要进行大数据量的计算,然而传统的CPU对大量的矢量计算并没有优势.为了解决这一问题,我们以CUDA作为并行计算架构,通过GPU-CPU并行编程技术,实现了三种快速高效的压缩感知图像恢复算法,包括正交匹配追踪OMP算法、两步阈值迭代TwIST算法和线性Bregman算法.
The signal reconstruction in compressive sensing （CS） requires a large amount of data processing. However the traditional CPU has no advantage on vector calculation. To deal with this issue, we design a parallel computing architecture using CUDA. Based on GPU-CPU parallel programming technology, and we realize three fast and efficient CS image reconstruction algorithms, including OMP, TWIST, and linear Bregman algorithm.
出处
《微电子学与计算机》
CSCD
北大核心
2016年第12期125-129,共5页
Microelectronics & Computer
基金
国家自然科学基金(61307200)
国防科技重点实验室基金(9140C380503140C38177)
