基于最小线性均方估计的小波去噪算法

提出一种新的基于最小线性估计正交小波图像去噪算法。该算法把降噪过程直接看作是一个小波系数的加权和，而不是为小波系数假设一个统计模型。在此，基于最小线性均方估计构造去噪函数，然后最小化均方误差，得到一组估计参数，从而得到线性去噪函数，实现对非线性去噪算法的改进，其最大的好处就是不用先验知识；最后通过使用该去噪算法对一定噪声级数的标准图像进行处理，并与目前去噪效果最好的BLS-GSM方法进行了比较。结果表明了该方法的有效性。

Wavelet De-noising Algorithm Based on LMS Estimation

A new orthogonal wavelet image de noising algorithm based on the least linear estimation is proposed. Instead of postulating a statistical model for the wavelet coefficients, the de-noising process was regarded as a sum of wavelet coefficients with unknown weights. Constructing de-noising function based on the least linear mean square estimation, then minimizing the mean square error, a group of estimation parameters can be acquired to construct the linear de-nosing function completing the improvement of no-linear de-nosing function. The most important advantage is that the priori knowledge is needless. Comparing with the result acquired by BLS-GSM over a suitable range of noise levels for a standard image, the result acquired by using this proposed approach.