Electronics and Control

SAR Image De-noising Based on Complex Shearlet Transform Domain Gaussian Mixture Model

  • LIU Shuaiqi ,
  • HU Shaohai ,
  • XIAO Yang
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  • Institute of Information Science, Beijing Jiaotong University, Beijing 100044, China

Received date: 2012-01-19

  Revised date: 2012-08-03

  Online published: 2013-01-19

Supported by

National Natural Science Foundation of China (60572093); Aeronautical Science Foundation of China (201120M5007); Research Fund for the Doctoral Program of Higher Education of China (20050004016)

Abstract

Using the characteristics of translation invariance, multi-resolution of dual tree complex wavelet transform and the more flexible, multi-selectivity of Shearlet transformation, this paper proposes a new algorithm called complex Shearlet transform. A Gaussian mixture model is introduced in order to capture the local coefficients of the complex Shearlets of synthetic aperture radar (SAR) images. The coefficients are estimated by Bayesian least squares estimator based on the model. Then, an inverse complex Shearlet transform is applied to the modified coefficients to get the SAR image after de-noising. The simulation effect and the analysis of the test results show that, compared with other de-noising methods, this algorithm has a better peak signal to noise ratio (PSNR) and the de-noised images are smoother. The computing speed is more than twice as fast as the method using the Shearlet domain Gaussian mixture model de-noising method.

Cite this article

LIU Shuaiqi , HU Shaohai , XIAO Yang . SAR Image De-noising Based on Complex Shearlet Transform Domain Gaussian Mixture Model[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2013 , 34(1) : 173 -180 . DOI: 10.7527/S1000-6893.2013.0020

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