Radar Imaging by Sparse Optimization Incorporating MRF Clustering Prior Article Swipe

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Shiyong Li , Moeness G. Amin , Guoqiang Zhao , Houjun Sun ·

YOU? · · 2018 · Open Access · · DOI: https://doi.org/10.48550/arxiv.1812.02366

Recent progress in compressive sensing states the importance of exploiting intrinsic structures in sparse signal reconstruction. In this letter, we propose a Markov random field (MRF) prior in conjunction with fast iterative shrinkagethresholding algorithm (FISTA) for image reconstruction. The MRF prior is used to represent the support of sparse signals with clustered nonzero coefficients. The proposed approach is applied to the inverse synthetic aperture radar (ISAR) imaging problem. Simulations and experimental results are provided to demonstrate the performance advantages of this approach in comparison with the standard FISTA and existing MRF-based methods.

Related Topics

Markov Random Field

Computer Science

Artificial Intelligence

Cluster Analysis

Synthetic-Aperture Radar

Mathematical Analysis

Image Segmentation

Concepts

Markov random field Compressed sensing Computer science Artificial intelligence Cluster analysis Inverse problem Iterative reconstruction Radar imaging Inverse synthetic aperture radar Synthetic aperture radar Radar Pattern recognition (psychology) Algorithm Computer vision Image (mathematics) Mathematics Telecommunications Mathematical analysis Image segmentation

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/1812.02366
PDF: https://arxiv.org/pdf/1812.02366
OA Status: green
References: 22
Related Works: 10
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DOI: https://doi.org/10.48550/arxiv.1812.02366

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Title: Radar Imaging by Sparse Optimization Incorporating MRF Clustering Prior

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Language: en

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Publication year: 2018

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Publication date: 2018-12-06

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Authors: Shiyong Li, Moeness G. Amin, Guoqiang Zhao, Houjun Sun

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Landing page: https://arxiv.org/abs/1812.02366

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Concepts: Markov random field, Compressed sensing, Computer science, Artificial intelligence, Cluster analysis, Inverse problem, Iterative reconstruction, Radar imaging, Inverse synthetic aperture radar, Synthetic aperture radar, Radar, Pattern recognition (psychology), Algorithm, Computer vision, Image (mathematics), Mathematics, Telecommunications, Mathematical analysis, Image segmentation

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abstract_inverted_index.sensing	4
abstract_inverted_index.signals	49
abstract_inverted_index.support	46
abstract_inverted_index.aperture	63
abstract_inverted_index.approach	56, 81
abstract_inverted_index.existing	89
abstract_inverted_index.methods.	91
abstract_inverted_index.problem.	67
abstract_inverted_index.progress	1
abstract_inverted_index.proposed	55
abstract_inverted_index.provided	73
abstract_inverted_index.standard	86
abstract_inverted_index.MRF-based	90
abstract_inverted_index.algorithm	33
abstract_inverted_index.clustered	51
abstract_inverted_index.intrinsic	10
abstract_inverted_index.iterative	31
abstract_inverted_index.represent	44
abstract_inverted_index.synthetic	62
abstract_inverted_index.advantages	78
abstract_inverted_index.comparison	83
abstract_inverted_index.exploiting	9
abstract_inverted_index.importance	7
abstract_inverted_index.structures	11
abstract_inverted_index.Simulations	68
abstract_inverted_index.compressive	3
abstract_inverted_index.conjunction	28
abstract_inverted_index.demonstrate	75
abstract_inverted_index.performance	77
abstract_inverted_index.experimental	70
abstract_inverted_index.coefficients.	53
abstract_inverted_index.reconstruction.	15, 37
abstract_inverted_index.shrinkagethresholding	32
cited_by_percentile_year
countries_distinct_count	2
institutions_distinct_count	4
citation_normalized_percentile