Open AccessReconstruction algorithm using exact tree projection for tree‐structured compressive sensing
Author(s) -
Wang Maojiao,
Wu Xiaohong,
Jing Wenhui,
He Xiaohai
Publication year - 2016
Publication title -
iet signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.384
H-Index - 42
ISSN - 1751-9683
DOI - 10.1049/iet-spr.2015.0351
Subject(s) - compressed sensing , algorithm , matching pursuit , tree (set theory) , computer science , sort , projection (relational algebra) , computational complexity theory , mathematics , pattern recognition (psychology) , artificial intelligence , mathematical analysis , information retrieval
Tree‐structured compressive sensing (CS) shows that it is possible to recover tree‐sparse signals using fewer measurements compared with conventional CS. However, performance guarantees rely heavily on the premise that an exact tree projection (ETP) algorithm is employed. Nevertheless, for a given sparsity, the condensing sort and select algorithm in the model‐based compressive sampling matching pursuit (CoSaMP) algorithm can only yield an approximate tree projection. Therefore, in order to ensure reconstruction precision, the authors propose the combination of an ETP algorithm with the CoSaMP algorithm. Further, the hierarchical wavelet connected tree is also integrated into the ETP‐CoSaMP algorithm to offset the high computational complexity of the ETP algorithm. Experimental results indicate that the hierarchical ETP based on CoSaMP algorithm (HETP‐CoSaMP algorithm) enhances reconstruction accuracy while retaining reconstruction time that is comparable with that of the model‐based CoSaMP algorithm.