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Despeckling SAR images based on a new probabilistic model in nonsubsampled contourlet transform domain

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Abstract

A method for synthetic aperture radar (SAR) image despeckling based on a probabilistic generative model in nonsubsampled contourlet transform (NSCT) domain was proposed. The shrinkage estimator in NSCT domain consists of a new type of likelihood ratio and prior ratio, both of which are dependent on the estimated masks for the NSCT coefficients. While the previous probabilistic approaches are restricted to parametric models, the limitation is eliminated and the hybrid density model is applied in this paper. The suggested approach does not make heavy assumptions on the NSCT coefficient distribution, so that it can handle complex NSCT coefficient structures. The likelihood ratio is composed of the hybrid density, and the prior ratio is equipped with the selective neighborhood systems to enhance the detail information. The method can effectively adapt the shrinkage estimator to the redundancy property of the NSCT. The proposed approach was applied to real SAR images despeckling and compared through the SAR image vision effect, the equivalent number of looks, and the edge sustain index. Experimental results show that the proposed approach outperforms previous works involved in the paper with the better despeckling result and edge preservation.

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Acknowledgments

This work is supported by the National Natural Science Foundation of China under grant No. 60972148, 61072106, 60970066, 61077009, 61075041, 61001206; the Fundamental Research Funds for the Central Universities under grant No. JY10000902043, JY10000902001, K50510020001, JY10000902045; Ph.D. Programs Foundation of Ministry of Education of China under grant No. 200807010003; the 111 Project under grant No. B07048; Beijing Municipal Natural Science Foundation under grant No. 7092020.

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  1. Key Laboratory of Intelligent Perception and Image Understanding, Ministry of Education of China, Institute of Intelligent Information Processing, Xidian University, Xi’an, 710071, People’s Republic of China

    Xiaolin Tian, Licheng Jiao & Xiaohua Zhang

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  1. Xiaolin Tian
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  2. Licheng Jiao
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  3. Xiaohua Zhang
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Correspondence to Xiaolin Tian.

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Tian, X., Jiao, L. & Zhang, X. Despeckling SAR images based on a new probabilistic model in nonsubsampled contourlet transform domain. SIViP 8, 1459–1474 (2014). https://doi.org/10.1007/s11760-012-0379-0

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  • DOI: https://doi.org/10.1007/s11760-012-0379-0

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