一种自适应的非局部均值图像去噪算法

摘要

In this paper, we make the improvements on non-local means (NL-Means) algorithm introduced by Buades et al .Original NL-Means algorithm has the defect in filtering parameter definition .In order to solve this problem , we present the solution for noise estimation by establishing the relation between noise variance and the filtering parameter .Besides , according to the distribution feature of wavelet coeffi-cients, the coefficients are fitted by using the generalised Gaussian distribution ( GGD) model parameters ( scale and shape parameters ) .We also propose an effective noise variance estimation method using GGD model parameters .Experimental results show that the noise variance es-timation method can effectively estimate the size of noise variance , it can also makes the original NL-means algorithm adaptive .Such adaptive NL-Means algorithm can reach approximately optimal value , and has robustness and fastness with high accuracy .%对Buades等人提出的非局部均值图像去噪算法进行改进。传统的方法在滤波参数定义上存在缺陷，为了解决这个问题，通过建立噪声方差与滤波系数的关系，提出解决噪声估计的方法。另外，根据小波系数的分布特点，利用GGD模型参数（尺度和形状参数）对系数进行拟合，并用GGD模型参数提出一种有效的噪声方差估计算法。实验结果表明，该噪声方差估计算法不仅能有效地估计噪声方差大小，而且使原有的非局部均值算法具有自适应性。这种自适应的非局部均值算法可以达到近似最优，具有鲁棒性和快速性，且算法精度高。