Title: Convex - Optimization for Reconstructing Compressed Signal using Orthogonal Matching Pursuit
Abstract:Reconstruction of 2D signals is challenging as it involves a large number of samples. To reduce the redundant data, a sparse representation technique called Compressed Sensing (CS) is explored. In com...Reconstruction of 2D signals is challenging as it involves a large number of samples. To reduce the redundant data, a sparse representation technique called Compressed Sensing (CS) is explored. In compressed sensing, sparse vector is compressed in size by using suitable sensing matrix. However reconstruction of original signal from compressed signal needs better understanding of sparseness of original signal and characteristics of sensing matrix. The reconstruction algorithm belongs to the category of convex optimization. This paper proposes an iterative greedy algorithm used in convex optimization technique for image reconstruction named Orthogonal Matching Pursuit (OMP). This work demonstrates how effectively we can use OMP to recover k-sparse signals in greedy fashion with lesser number of random linear measurements for a signal. The experimental results support the same.Read More
Publication Year: 2018
Publication Date: 2018-10-01
Language: en
Type: article
Indexed In: ['crossref']
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