基于局部Hu矩的非局部均值去噪算法

Non-local Mean Denoising Algorithm Based on Local Hu Moment

针对非局部均值中度量邻域块间相似性不够准确的问题,提出一种基于Krawtchouk多项式权重函数的局部Hu矩的去噪算法。将Krawtchouk多项式的权重函数与图像函数相结合构造几何矩的新的权重函数。利用几何矩权重函数得到新的中心矩。使用二阶和三阶中心矩构造7个不变矩组成特征矢量,通过欧式距离度量邻域间特征矢量的相似性,并与邻域块间的权重相结合得到新的权重。在不同噪声强度下的测试结果表明,与原始非局部均值去噪算法相比,该算法峰值信噪比与结构相似度都有明显提高。

Aiming at the problem that the similarity between neighborhood blocks in non-local means is not accurate,a local Hu moment denoising algorithm based on Krawtchouk polynomial weighting function is proposed. By combining the weighting function of Krawtchouk polynomial and image function, a geometric moment new weighting function is constructed. The new center moment is obtained by using the constructed geometrical moment weighting function. A set of featurevectors are constructed by using two moment invariants with second order and third order center moments. The Euclidean distance is used to measure the similarity of the feature vectors in the neighborhood and the new weighting is obtained by combining the weighting with the neighborhood blocks. Experimental results under different intensities of noise,show that compared with the original non-local mean noise denoising algorithm,the peak signal to noise ratio and structural similarity are significantly improved.