NDLI: Variable step-size matching pursuit based on oblique projection for compressed sensing

Content Provider	IET Digital Library
Author	Li, Na Yin, Xinghui Guo, Huanyin Zong, Sulan Fu, Wei
Abstract	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.
Starting Page	766
Ending Page	773
Page Count	8
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 4, Mar (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/4
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2019.0916
Journal	IET Image Processing
Publisher Date	2019-11-26
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Available Matching Pursuit Algorithm Compressed Sensing Compressive Sensing Computational Complexity Computer Vision And Image Processing Technique Greedy Algorithm High-computational Expenses Image Reconstruction Initial Sparsity Interpolation And Function Approximation Iterative Method Low-reconstruction Accuracy Matrix Algebra Novel Variable Step-size Matching Pursuit Numerical Analysis Oblique Projection Test Optical, Image And Video Signal Processing Optimisation Technique Reconstruction Efficiency Restricted Isometry Property Criterion Satisfactory Reconstruction Performance Sensing Matrix Signal Processing And Detection Signal Reconstruction Signal Sparsity Sparse Signal Reconstruction Sparsity Level Target Signal Variable Step Size VSMPOP Algorithm
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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