基于稀疏分解和非局部平均的乘性噪声图像滤波

项目名称： 基于稀疏分解和非局部平均的乘性噪声图像滤波

项目编号： No.61201448

项目类型： 青年科学基金项目

立项/批准年度： 2013

项目学科： 电子学与信息系统

项目作者： 陈少波

作者单位： 中南民族大学

项目金额： 24万元

中文摘要： 以稀疏分解和非局部均值为代表的图像去噪方法在含有加性噪声的图像中取得了很好的效果，但这两类方法对含有乘性噪声图像的处理却不尽如人意。本项目以SAR图像相干斑抑制为载体，分别研究这两类方法在含有乘性噪声图像中的应用。首先，研究基于非局部平均的斑点噪声抑制方法，利用SAR图像的变差系数修正欧式距离（相似性度量）。修正后的度量方式在乘性噪声模型中稳健性好，能在度量图像块之间的结构相似性的同时刻画两个图像块的异质性。其次，根据乘性噪声模型修改"Michael Elad模型"的数据保持项，提出基于稀疏分解的正则化乘性噪声抑制模型；应用于斑点噪声抑制，提高图像的细节信息保持度。最后，结合稀疏分解和非局部思想的优点，设计基于稀疏分解和非局部变分的去噪模型。该方法既能减少计算复杂度，又能更好地恢复小图像块在拼接过程形成的缝隙区域的纹理、细节等信息。研究成果将会补充以稀疏分解和非局部均值为基础的图像去噪方法

中文关键词： 乘性噪声；滤波；非局部平均；稀疏分解；异质性测量

英文摘要： The image denosing methods based on sparse representation and nonlocal mean have been well studied for additive noise reducing, and experiments show that these methods do good job in this case. However, they become invalid when facing multiplicative image noise. So, this study search for filtering algorithms based on sparse representation and nonlocal mean, in order to suppress the multiplicative noise in image such as Synthetic aperature radar (SAR). Firstly, the speckle filter based on nonlocal mean will be studied. We will use the the local coefficient of variation (CV) to correct the euclidean distance (the image patches' similarity measurement). The revised measurement is robust in multiplicative noise model, and it can index both the structural similarity and the heterogeneity between image patches. Secondly, the data fidelity term of "Michael Elad model " will be altered according to multiplicative noise model, then a novel regularization filter model based on sparse representaion will be presented; when being used to process the SAR image, it can effectively reduce the speckle and protect the details in SAR image simultaneously. Lastly, the novel filter based on nonlocal varation denosing model and sparse representaion will be studied for supressing multiplicative noise in image; this method take the ad

英文关键词： multiplicative noise；filtering；sparse decomposition；nonlocal mean；heterogeneity measurement