摘要
当前压缩感知中测量矩阵的优化是测量阶段和重构阶段采用同一矩阵的事前优化。采用了以行变换为主的测量矩阵优化算法和过渡矩阵将压缩感知的测量矩阵和重构矩阵相分离,在测量阶段采用单像素相机的0-1稀疏矩阵,在重构阶段采用近似矩阵,这是区别于传统思路的测量数据和测量矩阵的事后优化方法。理论分析和实验结果表明,优化矩阵的性能好于稀疏循环矩阵,近似矩阵和优化矩阵具有相近的性能。研究成果降低了测量矩阵工程设计和实现的难度。
Current optimization of measurement matrix of compressive sensing is optimization beforehand by using the same matrix in measurement and reconstruction stages. Transition matrix and optimization algorithm mainly based on row transformation are proposed to separate the measurement matrix and reconstruction matrix of compressive sensing. 0-1 sparse matrix of single-pixel camera is adopted during measurement, while approximate matrix is adopted during reconstruction. It is a kind of afterwards optimization method of measurement data and measurement matrix, different from traditional thinking. Theory analysis and experiment results demonstrate that the characteristics of optimal matrix are better than circulant sparse matrix, and approximate matrix and optimal matrix have similar characteristics. The research results reduce the difficulty of engineering design and implementation of measurement matrix.
出处
《光学学报》
EI
CAS
CSCD
北大核心
2013年第2期165-170,共6页
Acta Optica Sinica
基金
国家自然科学基金(40871201)资助课题
关键词
压缩感知
测量矩阵
重构矩阵
过渡矩阵
相关性
compressive sensing
measurement matrix
reconstruction matrix
transition matrix
relevance
