基于梯度特征的双核非局部均值去噪算法

针对传统非局部均值(NLM)滤波算法中邻域间相似性计算易受噪声干扰的问题,提出了一种基于梯度特征的双核非局部均值滤波算法。通过图像块之间的欧氏距离及梯度特征度量邻域间相似性,采用双核函数代替传统指数核函数计算相似性权值,并通过衡量搜索区域中的邻域块与当前像素邻域的相似程度,对像素点的权值进行重分配,在此基础上重估像素点去噪值并得到滤波图像。实验结果表明,提出的滤波算法与传统的NLM滤波算法及分别含有高斯核和正弦核的改进NLM滤波算法相比,可以更准确地反映邻域间的相似度,保存图像的细节及边缘信息,从而有效提升图像的去噪效果。

Dual kernel non-local means denoising algorithm based on gradient feature

In the traditional non-local mean (NLM) filtering algorithm, the presence of noise in the image interferes the accuracy of similarity calculation between neighborhood blocks. To address this problem, this paper proposed a dual kernel non-local means denoising algorithm based on gradient feature. This algorithm calculates the similarity of neighborhood block by Euclidean distance and gradient feature between pixels, and replaces the original exponential function with dual kernel function to calculate similar weights. In addition, it measures the similarity between the neighborhood blocks of the pixels in the search area and the current neighborhood to reassign the pixels'' weight. On this basis, it reevaluates the denoising values and get the denoised image. Experimental results show that this algorithm can accurately reflect the similarity between neighborhood block of pixels and preserve the details and edge information of images effectively while compared with the traditional NLM filtering algorithm and two other improved NLM algorithms, and improve the effect of image denoising significantly.