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中文摘要：
从SAR图像相干斑噪声的统计特点出发，将Curvelet变换与隐马尔可夫树（HMT）模型相结合，提出了一种基于Curvelet域隐马尔可夫树（HMT）模型的图像去噪方法．利用HMT模型捕获Curvelet系数之间的尺度从属性，较好地实现了普通图像去噪和SAR图像的相干斑噪声抑制，同时分析了文中算法的去噪机理和计算复杂度．仿真实验证明，与小波域HMT模型方法和Curvelet变换方法比较，主观视觉效果和数值指标都有明显改进．平滑指数（FI）值大小适中，水平和垂直边缘保持指数（ESI）平均提高了约0．2～0.3．
英文摘要：
Based on the statistical property of SAR image speckle noise and combining curvelet transform with HMT models, a method of SAR image de-noising based on curvelet domain hidden Markov tree （HMT） models is presented in this paper. Using HMT models to capture the scale dependencies among curvelet coefficients, it implements the image de-noising and reduces SAR Speckle noise effectively, furthermore, analyze the algorithm mechanism and computation complexity. De-noising performance is evaluated through subjective inspection, as well as objective measurements-flatness index and edge save index. Results clearly demonstrate the superiority of this new approach when compared to conventional wavelet domain HMT and curvelet transform. FI value obtained is appropriate and ESI vertically and horizontally are averagely increased by about from 0.2 to 0. 3.
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