基于LS-SVR的图像噪声去除算法研究

通过对最小二乘支持向量机(Least squares support vector regression, LS-SVR)滤波特性的分析, 给出了LS-SVR用于图像滤波的卷积模板构造方法, 解决了LS-SVR在应用中需要求解的问题, 在此基础上, 提出了基于LS-SVR的开关型椒盐噪声滤波算法. 滤波算法中以Maximum-minimum算子作为椒盐噪声检测器, 利用滤波窗口内非噪声点构成LS-SVR的输入数据, 使用事先构造出的LS-SVR滤波算子, 对滤波窗口进行简单的卷积运算, 实现了被椒盐噪声污染点数据的有效恢复. 实验表明, 本文提出的方法具有较好的细节保护能力和较强的噪声去除能力.

Research on Image Noise Suppression Algorithm Based on LS-SVR

Through analyzing the least squares support vector regression (LS-SVR) filtering characteristics, the LS-SVR method is presented to construct the convolution mask of image filtering, which resolves the solver problem in the LS-SVR application. Based on the method, the LS-SVR switching filter for image corrupted by salt & pepper noise is proposed. By taking the maximum-minimum operator as the salt & pepper noise detector in the filtering algorithm, using the non-noise point of the filtering windows as the input data set of the LS-SVR, and utilizing the LS-SVR filtering operator to execute simple convolution operation on the filtering windows, the corrupted data are efficiently restored. Experiments show that the proposed algorithm has better detail preserving ability and better noise removing ability.