摘要
为提高基于压缩感知的图像重建质量,利用改进测量阵结合压缩采样匹配追踪(CoSaMP)算法,提出一种傅立叶类圆环压缩采样匹配追踪FR-CoSaMP算法。通过调整测量矩阵的结构,拓宽测量范围,使其更加完整准确地选取保留图像的有用信息,着重介绍FR-CoSaMP算法的数学框架和算法流程。与传统的图像重建算法进行分析比较及仿真实验,实验结果表明,在相同的图像稀疏度和测量比例的情况下,FR-CoSaMP算法具有良好的图像重构效果。
For improving the quality of the image reconstruction based on compressed sensing algorithm,the improved measurement matrix was used combined with compressive sampling matching pursuit（CoSaMP）algorithm,the Fourier ring compressive sampling matching pursuit（FR-CoSaMP）algorithm was put forward.The measurement matrix structure was adjusted and the measuring range was broadened to keep the useful image information more complete and accurate.Also the mathematical framework and algorithmic processes of the FR-CoSaMP algorithm were emphatically introduced.Results of analyzing and simulating show that compared with traditional algorithms,the improved FR-CoSaMP algorithm has excellent effects on image reconstruction.
出处
《计算机工程与设计》
北大核心
2016年第6期1555-1559,共5页
Computer Engineering and Design
基金
国家自然科学基金项目(61164015)
江西省教育厅科技落地计划基金项目(DB201304126)
江西省教育厅科技支撑计划基金项目(DB201404071)
江西省科技支撑计划基金项目(CB201504091)
关键词
压缩感知
测量矩阵
傅里叶类圆环
匹配追踪
图像重建
compressed sensing
measurement matrix
Fourier ring
orthogonal matching
image reconstruction
