Fast compression algorithm of SAR image based on compressed sensing

Author

Lina Guo ; Xianbin Wen ; Jinjin Yu

Author_Institution

Key Lab. of Comput. Vision & Syst. of Minist. of Educ., Tianjin Univ. of Technol., Tianjin, China

fYear

2013

fDate

9-11 June 2013

Firstpage

144

Lastpage

149

Abstract

A novel SAR image compress and reconstruction algorithm based on compressive sampling (CS) is proposed in this paper. Firstly, the image is represented sparsely by G-level contourlet. Secondly, a Gaussian random matrices that proximate QR factorization is constructed to measure the high frequency coefficients and to realize data compression. Lastly, a modified Sparsity Adaptive Matching Pursuit algorithm(SAMP) is used to realize the precise reconstruction of SAR image. Experimental results demonstrate that the proposed algorithm can get better reconstruction performances and the convergence of the algorithm is much faster than the existed algorithms.

Keywords

compressed sensing; image matching; image reconstruction; radar imaging; synthetic aperture radar; CS; G-level contourlet; Gaussian random matrices; QR factorization; SAMP; SAR image; compressed sensing; compressive sampling; data compression; fast compression algorithm; high frequency coefficient; image reconstruction algorithm; sparsity adaptive matching pursuit algorithm; synthetic aperture radar; Compressed sensing; Image coding; Image reconstruction; Image resolution; Matching pursuit algorithms; Matrix decomposition; Transforms; SAR image; compressed sensing; contourlet transform; proximate QR factorization; sparsity Adaptive Matching Pursuit algorithm;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Information Processing (ICICIP), 2013 Fourth International Conference on

Conference_Location

Beijing

Print_ISBN

978-1-4673-6248-1

Type

conf

DOI

10.1109/ICICIP.2013.6568057

Filename

6568057