JI Jian and LI Yang, “An Improved SAR Image Denoising Method Based on Bootstrap Statistical Estimation with ICA Basis,” Chinese Journal of Electronics, vol. 25, no. 4, pp. 786-792, 2016, doi: 10.1049/cje.2016.06.040
Citation: JI Jian and LI Yang, “An Improved SAR Image Denoising Method Based on Bootstrap Statistical Estimation with ICA Basis,” Chinese Journal of Electronics, vol. 25, no. 4, pp. 786-792, 2016, doi: 10.1049/cje.2016.06.040

An Improved SAR Image Denoising Method Based on Bootstrap Statistical Estimation with ICA Basis

doi: 10.1049/cje.2016.06.040
Funds:  This work was supported by the National Natural Science Foundation of China (No.61101248) and the Fundamental Research Funds for the Central Universities of China (No.JB160302, No.JB150317).
  • Received Date: 2014-06-26
  • Rev Recd Date: 2014-06-26
  • Publish Date: 2016-07-10
  • A new method for Synthetic aperture radar (SAR) image denoising is proposed. The prior information of speckle statistical model can be exploited to judge its distribution. The basis of SAR image can be estimated by Independent component analysis (ICA), and these bases can be divided into two different subspaces (noise and real signal subspaces) through a linear classifier. Then parametric Bootstrap estimates the parameters of speckle statistical model on the noise signal subspace, and the nonparametric Bootstrap can estimate the distribution of real image on the real signal subspace. According to different results estimated by Bootstrap, corresponding Maximum a posterior probability (MAP) filter will be selected for image denoising, using the noise model's parameter for adaptive filtering. Experiments show that the image processed by this new method can achieve a better visual perception and objective evaluation results.
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