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.
Recommended Citation
D. Bi et al., "Efficient 2-D Synthetic Aperture Radar Image Reconstruction from Compressed Sampling using a Parallel Operator Splitting Structure," Digital Signal Processing: A Review Journal, pp. 171 - 179, Elsevier, Mar 2016.
The definitive version is available at https://doi.org/10.1016/j.dsp.2015.12.014
Department(s)
Electrical and Computer Engineering
Sponsor(s)
National Natural Science Foundation of China
Specialized Research Fund for the Doctoral Program of High Education of China
National Basic Research Program of China
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
International Standard Serial Number (ISSN)
1051-2004
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2016 Elsevier, All rights reserved.
Publication Date
01 Mar 2016
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).