A NOVEL BAYESIAN COMPRESSED SENSING ALGORITHM USING SPARSE TREE REPRESENTATION
Compressed Sensing (CS) is a novel emerged theory in the last several years in the area of signal processing. CS could recover the signal correctly by sampling a sparse signal below the Nyquist rate. Bayesian Compressed Sensing (BCS) is a new framework in CS which recovery performance is proved to be close to L0-norm solution. Recent studies have recognized that in many multiscale bases such as wavelets, signals of interest have not only few significant coefficients, but also a wellorganized tree structure of those significant coefficients. In this paper, we exploit the tree structure as additional prior information to the framework of the BCS, and then propose a novel BCS algorithm for signal reconstruction with limited number of measurements. Simulation results indicate that exploiting the proposed BCS algorithm using the sparse tree representation could reduce the required number of iterations greatly, and achieve better reconstruction as well as faster iteration speed compared to original BCS algorithm.
Compressed Sensing Bayesian Compressed Sensing wavelet tree structure
Zhen Zheng Wenbo Xu Kai Niu Zhiqiang He Baoyu Tian
Key Laboratory of Universal Wireless Communication, Ministry of Education,Beijing University of Posts and Telecommunications, Beijing 100876, China
国际会议
深圳
英文
178-182
2011-10-28（万方平台首次上网日期，不代表论文的发表时间）