Open AccessVariable step‐size matching pursuit based on oblique projection for compressed sensing
Author(s) -
Li Na,
Yin Xinghui,
Guo Huanyin,
Zong Sulan,
Fu Wei
Publication year - 2020
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2019.0916
Subject(s) - matching pursuit , compressed sensing , computational complexity theory , algorithm , greedy algorithm , computer science , restricted isometry property , matching (statistics) , projection (relational algebra) , signal reconstruction , variable (mathematics) , signal (programming language) , mathematics , artificial intelligence , signal processing , telecommunications , mathematical analysis , radar , statistics , programming language
The development of compressive sensing has focused on sparse signal reconstruction in recent years. Most existing greedy algorithms achieve satisfactory reconstruction performance only when the sparsity of the target signal has been known as prior information. Moreover, some greedy algorithms always involve either high‐computational expenses or low‐reconstruction accuracy caused by the process of adaptive adjustment of signal sparsity. To address these concerns, a novel variable step‐size matching pursuit based on oblique projection (VSMPOP) for compressed sensing is proposed. The proposed VSMPOP algorithm estimates the initial sparsity based on the restricted isometry property criterion. The algorithm creates a support set of the target signal after a preliminary test and oblique projection test between the sensing matrix and the residual. VSMPOP realises a similar approach to the sparsity level with a variable step size. The experimental results demonstrated that the proposed VSMPOP algorithm provides superior performance in terms of computational complexity and reconstruction efficiency compared with most of the available matching pursuit algorithms.