文章对非局部均值（NL-Means) 图像去噪算法进行了改进，提出一种定量估计算法滤波参数最优值的方法，由噪声图像估计噪声方差，进而由噪声方差与图像方差估计滤波参数h。另外，根据局部区域加权欧氏距离的对称性，将算法中复杂度最高的两像素间距离计算由两次降为一次，从而在不损失性能的条件下使计算复杂度降低到原来的一半左右。在多个典型图像上的实验结果表明，提出的自适应非局部均值算法(ANL-Means)可达到近似最优性能，且处理时间只有标准NL-Means算法的一半左右。

This paper makes some improvements on Non Local Means（NL-Means) image denoising algorithm. A quantitative method is given to estimate the optimal filter parameter h. Noise variance is estimated from noise image, and h parameter is estimated from this variance and noise image standard deviation. Based on the symmetry of weighted Euclidean distance, the most complex distance computation was halved for every pair of pixels, so the computation complexity was reduced to about half of original NL-Means without performance decline. Experimental results on several images show that our adaptive non local means algorithm（ANL-Means) gives nearly best performance with only about half computation time if using original NL-Means.