【作者】 许光宇； 檀结庆； 钟金琴；

【Author】 Xu Guangyu~1,Tan Jieqin~(1,2),Zhong Jinqin~1 1(School of Computer & Information,HFUT, Hefei,230009,China) 2(School of Mathematics of HFUT, Hefei,230009,China)

【机构】 合肥工业大学计算机与信息学院； 合肥工业大学数学学院；

【摘要】 非局部均值滤波方法具有优异的去噪性能,但其计算复杂度太高,且滤波后图像有大量结构残留。本文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提取图像子块结构特征,提出一种有效保持细节特征的快速非局部滤波方法。该方法主要贡献有:1)基于局部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,以及相似子块的自动选取;3)构造结构相似权系数,并耦合到加权平均过程中。此外,利用欧氏距离的对称性进一步提高运行速度。实验表明:本文方法在去除噪声的同时能够有效的保持图像细节信息,优于原算法与其它改进方法,取得滤波性能与运行速度之间较好的平衡。更多还原

【Abstract】 Nonlocal Means filtering(NLMF) method exhibits exciting performance for image denoising. However,the computational complexity of NLMF is too high,and some structure content of the original image is still visible in the residual noise.The existing NLMFs based preselection are analyzed,and it is pointed out that they all have deficiencies in terms of feature extraction from image patch,then a detail-preserving and fast NLMF is proposed via using the singular value decomposition(SVD) in gradient domain to extract structure feature from image patch.Our contributions to NLMF are:1) a robust preselection approach to image patches based local structure feature;2) to analyze relation between size of the similar sets and filtering performance,and automatic selection of similar patches; and 3) to construct the weight coefficient with structural similarity,and coupled into the weighted average.In addition,the symmetry of Euclidean distance is considered to accelerate further.The experimental results show that the proposed method can well remove the noise while preserving image details,and outperforms the original NLMF and other improved method,and that obtains a good tradeoff between performance and running speed.更多还原