研究了基于预选择的非局部均值滤波方法,并指出已有方法在提取图像子块特征方面的不足,利用梯度域奇异值分解(SVD) 提出一种自适应的有效非局部滤波方法。该方法对基于预选择的非局部滤波方法的贡献主要有:1)是一种基于图像子块结构特征的鲁棒预选择方法;2)研究了相似集大小与滤波性能的关系;3)相似子块的自动选取;4)提出一种局部自适应的滤波参数选取方法,根据图像局部内容的不同确定滤波参数。此外,利用欧氏距离的对称性进一步提高运行速度。实验结果表明:本文方法滤波后的图像在主客观方面都优于原算法和其他快速方法,且运行速度较快,是一种有效的滤波方法。

Non-local Means Filtering (NLMF) has been a popular issue in the image filtering field.The existing NLMFs based pre-selections are analyzed,and it is pointed out that they all have deficiencies in terms of feature extraction from image patches.An adaptive and effeicient NLMF method is proposed using singular value decomposition (SVD) in the gradient domain.Our contributions to NLMF based pre-selection are:1)the robust pre-selection method based structure feature from image patch;2)the relation between size of the similar sets and filtering performance is analyzed;3)automatic selection of similar patches;4)local adaptive selection of the filtering parameter.In addition,the symmetry of the Euclidean distance is considered to accelerate the proposed method further.The experimental results show that the proposed method outperforms the original NLMF and other fast NLMFs on subjective and objective aspects,and has rapid running speed.The proposed method is an efficient filtering method.