关键词: 非局部滤波, 梯度域奇异值分解, 图像特征, 预选择, 结构相似权系数

Abstract: Nonlocal Means Filtering（NLMF） method exhibits excellent 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 algorithm is proposed via using the Singular Value Decomposition（SVD） in gradient domain to extract structure feature from image patch. The 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. In addition, the symmetry of Euclidean distance is considered to accelerate the proposed algorithm further. The experimental results show that the proposed algorithm can well remove the noise while preserving image details and obtains a good tradeoff between performance and running speed.

Key words: Nonlocal Means Filtering（NLMF）, Singular Value Decomposition（SVD） in gradient domain, image feature, preselection, structure similarity weight