基于Membrane MRF模型的SAR图像贝叶斯去斑

SAR图像可以看作是真实反映地物后向散射特性的无噪图像与相干斑噪声的乘积,通过贝叶斯估计从图像观测值估计出图像真值即可去除相干斑.而贝叶斯去斑的关键在于建立能与SAR图像特性相匹配的先验信息模型.用MembraneMRF模型对先验信息建模,克服了以往所用GMRF模型对参数估计十分敏感的问题,并通过对该模型邻域结构的自适应调整来分类处理处于匀质区域和含结构特征区域的像元,在有效抑制相干斑的同时较好地保持图像的结构特征.仿真和实际SAR图像数据的实验结果,验证了所提方法的有效性.

Bayesian despeckling of SAR images based on the Membrane MRF prior model

A SAR image can be modeled as the multiplication of the noise-free image and speckles. So the noise-free image can be estimated from the observed image with the Bayesian technique. 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 GMRF's problem of sensitivity to parameters. And, pixels in homogeneous and non-homogeneous regions are processed separately by adjusting the model's neighborhood adaptively. Experiments show that not SAR images can be despeckled efficiently while their structures are preserved well.