一种改进的非局部均值去噪算法*

针对非局部均值去噪算法在图像块相似度计算方面存在的不足，提出计入图像旋转对相似度贡献的、效果更好的图像块匹配算法.为了获得与给定像素点邻域相似的图像子块，首先对给定像素点周边的相关邻域子块按灰度值大小排序，计算其与同样按灰度值大小排序的给定像素点邻域子块之间的距离，据此筛选出灰度分布相似的图像子块作为候选集，更进一步在候选集中选出结构上更为相似的图像子块.同时为了克服噪声影响，在计算子块相似度之前对输入图像进行预滤波处理.实验表明，与原始的非局部均值去噪算法相比，文中算法在峰值信噪比、平均结构相似性及主观视觉效果等方面均具有一定优势，特别是在噪声较大时，文中算法的去噪效果更好.

An Improved Non-local Means Denoising Algorithm

Aiming at the problem of similarity calculation for image block in non-local means (NLM) denoising algorithm, a more accurate block matching algorithm is proposed. In this algorithm, the contribution of rotation is taken into alcount. To obtain the blocks similar to the neighborhood of the given pixel, the related blocks surrounding the given pixel are reordered according to their gray values, and then the pixels in the neighborhood of the given pixel are also reordered in the same way. Finally, the distance between the related blocks and the neighborhood of the given pixel are calculated according to the reordered gray values. The candidate blocks with small distance are selected. Furthermore, the more structurally similar blocks are selected from the candidate blocks. To eliminate the effect of noise, the inputted image is processed by a pre-filtering operation before similarity calculation. Simulation experiments show that compared with the original NLM denoising algorithm, the proposed algorithm has better performances in peak signal-to-noise ratio (PSNR), mean structural similarity (MSSIM) and subjective visual effect. Especially, the proposed algorithm has better denoising performance for the images with lots of noise variance.