Title

Efficient 2-D Synthetic Aperture Radar Image Reconstruction from Compressed Sampling using a Parallel Operator Splitting Structure

Abstract

This paper investigates an efficient compressed sensing (CS) approach that can be used to reconstruct 2-D millimeter-wave synthetic aperture radar (SAR) images from under-sampled measurements. This approach minimizes a linear combination of four terms corresponding to a least squares data fitting, â""1 norm regularization, total variation (TV) and a bounding operator. Although the strong convergence of this approach cannot be guaranteed, this approach always converges to a stable structural similarity (SSIM) value with a combination of a parallel operator splitting structure and a FISTA (Fast Iterative Shrinkage-Thresholding Algorithm) updating stage. Simulation and experimental results demonstrate the superior performance of the proposed approach in terms of both efficiency and computation complexity.

Department(s)

Electrical and Computer Engineering

Sponsor(s)

National Natural Science Foundation of ChinaSpecialized Research Fund for the Doctoral Program of High Education of ChinaNational Basic Research Program of China

Comments

This work was supported by the National Natural Science Foundation of China (61371049 & 60871056), the Specialized Research Fund for the Doctoral Program of High Education of China (20120185110013) and the National Basic Research Program of China (973 Program) (Grant no. 2014CB744206).

Keywords and Phrases

Compressed sensing; Image processing; Image reconstruction; Iterative methods; Millimeter waves; Radar; Radar imaging; Radar measurement; Radar signal processing; Signal reconstruction; Compressed samplings; Compressive sensing; Computation complexity; Iterative shrinkage-thresholding algorithms; Least-squares data-fitting; Linear combinations; Structural similarity; Synthetic aperture radar (SAR) images; Synthetic aperture radar; 2-D radar imaging; Compressed sensing (CS); Millimeter-wave; Synthetic aperture radar (SAR)

International Standard Serial Number (ISSN)

1051-2004

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2016 Elsevier Inc., All rights reserved.

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