小波-Contourlet 与迭代Cycle Spinning 相结合的SAR 图像去噪

doi:10.3969/j.issn.0255-8297.2014.06.009

Abstract

Abstract: By analyzing a speckle model of synthetic aperture radar (SAR), a de-noising method for SAR
images based on the wavelet-Contourlet transform and recursive cycle spinning is presented. Compared with
wavelet transform and Contourlet transform, wavelet-Contourlet transform can express images more sparsely
and better obtain image structure. Because the Contourlet transform lacks shift invariance, wavelet-Contourlet
transform also lacks shift invariance. Threshold processing on the coefficients may produce pseudo Gibbs
phenomena. Although a cycle spinning algorithm can reduce the pseudo Gibbs phenomena, it is not the
best. In this paper, wavelet transform is used to replace the Laplacian pyramid transform (LPT) for sub-band
decomposition. Recursive cycle spinning is used to replace the cycle spinning. Simulation results show that
the proposed algorithm is efficient, and it performs significantly better in reducing speckle noise, resulting in
higher peak signal-to-noise ratio, more image details and better visual quality.

Key words: SAR image, de-noising, Contourlet, wavelet-Contourlet, recursive Cycle spinning

CLC Number:

TN911.6

FANG Jing1,2, XIAO Yang1, WANG Dong1. De-noising of SAR Images Based on Wavelet-Contourlet Transform with Recursive Cycle Spinning[J]. Journal of Applied Sciences, 2014, 32(6): 605-610.

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De-noising of SAR Images Based on Wavelet-Contourlet Transform with Recursive Cycle Spinning

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