基于局部边沿方向的非局部均值图像去噪算法

摘要：经典的非局部均值(Classicnon-localmeans,CNLM)算法利用图像中自有的自相似性对噪声进行去除。去噪像素值通过其非局部邻域中所有像素的加权平均而得到。在CNLM 算法中,计算权重系数的过程中同时考虑了像素值和该像素点距离中心像素点的距离之间的差异。然而,由于各向同性,高斯核不能反应边沿和结构信息,并且高斯核在平坦区域表现欠佳。本文提出了一种改进的基于局部边沿方向的非局部均值图像去噪算法。在边沿和结构区域使用旋转核回归系数(Steeringkernelregression,SKR)计算权重系数,在平坦区域使用平均核计算权重系数。实验结果表明,与CNLM 算法相比,提出的算法可以在有效地保护边沿和结构的同时更好地去除噪声。
Abstract： Classic non-local means (CNLM) algorithm uses the inherent self-similarity in images for noise removal. The denoised pixel value is estimated through the weighted average of all the pixels in its non-local neighborhood. In the CNLM algorithm, the differences between the pixel value and the distance of the pixel to the center are both taken into consideration to calculate the weighting coefficients. However, the Gaussian kernel cannot reflect the information of edge and structure due to its isotropy, and it has poor performance in flat regions. In this paper, an improved non-local means algorithm based on local edge direction is presented for image denoising. In edge and structure regions, the steering kernel regression (SKR) coefficients are used to calculate the weights, and in flat regions the average kernel is used. Experiments show that the proposed algorithm can effectively protect edge and structure while removing noises better when compared with the CNLM algorithm.