SAR Image Despeckling Using Scale Mixtures of Gaussians in the Nonsubsampled Contourlet Domain

CHANG Xia; JIAO Licheng; LIU Fang; SHA Yuheng

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Article Navigation > Chinese Journal of Electronics > 2015 > 24(1): 205-211

CHANG Xia, JIAO Licheng, LIU Fang, et al., “SAR Image Despeckling Using Scale Mixtures of Gaussians in the Nonsubsampled Contourlet Domain,” Chinese Journal of Electronics, vol. 24, no. 1, pp. 205-211, 2015,

Citation:

CHANG Xia, JIAO Licheng, LIU Fang, et al., “SAR Image Despeckling Using Scale Mixtures of Gaussians in the Nonsubsampled Contourlet Domain,” Chinese Journal of Electronics, vol. 24, no. 1, pp. 205-211, 2015,

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SAR Image Despeckling Using Scale Mixtures of Gaussians in the Nonsubsampled Contourlet Domain

1.
Institute of Image Processing and Understanding, School of Mathematics and Information Science, Beifang University of Nationalities, Yinchuan 750021, China;
2.
Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, International Research Center for Intelligent Perception and Computation, Xidian University, Xi'an 710071, China;
3.
School of Computer Science and Technology, Xidian University, Xi'an 710071, China

Funds: This work is supported by the National Natural Science Foundation of China (No.61102008, No.61102095, No.61163017, No.61173090, No.61261043, No.61440044), the Open Research Fund Program of Key Laboratary of Intelligent Perception and Image Understanding of Ministry of Education of China (No.IPIU012011006, No.IPIU01201108), the Science Research Project of Beifang University of Nationalities (No.2100Y021), and the Ningxia Science Foundation of China (No.NZ13097).

Received Date: 2013-05-01
Rev Recd Date: 2013-08-01
Publish Date: 2015-01-10

Abstract

Abstract

The edge and contour details in SAR images are important for subsequent processing tasks. The multiscale geometric analysis method —— Nonsubsampled contourlet transform (NSCT) is able to capture the geometric information of SAR images effectively. Describing the aggregation behavior of the neighborhoods coefficients, the scale mixtures of Gaussians model has exhibited favorable performances. A novel SAR image despeckling method is presented by constructing the scale mixtures of Gaussians model of NSCT. This method models the SAR images using the multiscale and multidirection information in NSCT domain. The dependency relationship of NSCT neighborhoods coefficients are also taken into consideration in our model. The speckle noise coefficients are shrinkaged by statistical prior estimation based on SAR image model constructed. Experimental results demonstrate that our method is advantageous at directional information preservation and the speckle restraint.

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References(14)

References

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