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Bayesian Despeckling to SAR Images Based on the Membrane MRF Model

机译:基于膜MRF模型的贝叶斯去斑SAR图像

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SAR imagery can be modeled as the multiplication of the noise-free image and speckle noises. So the noisefree image can be estimated from the observed image with the Bayesian estimation. It’s crucial to choose a proper prior model for well matching the SAR images’ characteristics. In this article the Membrane MRF model is employed to model the prior information, which overcomes the GMRF’s sensitivity to parameters. Simultaneously, the pixels in the homogeneous or in regions with structures are processed by adjusting the model’s neighborhood adaptively. Experiments show that not only the image is despeckled effectively but also the structures are preserved well.
机译:SAR图像可以建模为无噪声图像和斑点噪声的乘积。因此,可以利用贝叶斯估计从观察到的图像中估计出无噪声图像。选择合适的先验模型以很好地匹配SAR图像的特征至关重要。本文采用膜MRF模型对先验信息进行建模,从而克服了GMRF对参数的敏感性。同时,通过自适应调整模型的邻域来处理同质或具有结构的区域中的像素。实验表明,不仅有效地去除了图像的斑点,而且结构得到了很好的保存。

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