Volume 8 Number 4 (Apr. 2013)
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JSW 2013 Vol.8(4): 947-954 ISSN: 1796-217X
doi: 10.4304/jsw.8.4.947-954

Speckle Suppression Method in SAR Image Based on Curvelet Domain BivaShrink Model

Wenbo Wang1, Xiaodong Zhang2, 3, Xiangli Wang4
1College of Science, Wuhan University of Science and Technology, Wuhan,430065, China
2State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing, Wuhan, 430072, China
3State Key Laboratory of Satellite Ocean Environment Dynamics,Second Institute of Oceanography State Oceanic Administration, Hangzhou, China

4School of Computer Science and Technology, Wuhan University of Technology, Wuhan 430063,China

Abstract—Based on the statistical property of SAR image speckle noise and the property that the multiscale geometric analysis can capture the intrinsic geometrical structure of image, combining curvelet transform with BivaShrink denoising model, a method of SAR image denoising based on curvelet domain is presented in this paper. According to calculation of variance homogeneous measurement and curvelet coefficients of current layer and its parent layer, the local adaptive window is determined to optimally estimate shrinkage factor. The method can effectively reduce SAR speckle noise and preserving details of SAR image well through the correlation of curvelet coefficients in the same direction of subband and parent-layer subband. The experimental results show the presented method greatly improves the subjective visual effect and the numerical indicators of the denoised image.

Index Terms—Curvelet transform, Variance homogeneous measurement, speckle suppression, SAR image.

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Cite: Wenbo Wang, Xiaodong Zhang, Xiangli Wang, "Speckle Suppression Method in SAR Image Based on Curvelet Domain BivaShrink Model," Journal of Software vol. 8, no. 4, pp. 947-954, 2013.

General Information

ISSN: 1796-217X (Online)
Frequency:  Bimonthly (Since 2020)
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, Google Scholar, ProQuest, INSPEC(IET), ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsw@iap.org
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