基于方向增强邻域窗和非下采样Shearlet描述子的非局部均值图像去噪

A Non-local Means Filter Image Denoising with Directional Enhancement Neighborhood Windows and Non-subsampled Shearlet Feature Descriptors

非局部均值(Non-Local Means,NLM)是一种有效的图像去噪方法。然而它仅关注图像的几何结构信息而忽略了图像表面模型和方向信息,其相似性度量鲁棒性差。针对这些缺点,该文首先提出了一种基于非下采样的Shearlet的描述子(NSSD),它能更好地描述图像块的特征,基于此构造的相似性度量具有较强的鲁棒性。本文基于此描述子与非局部计算模型提出了一种更加有效的非局部均值去噪算法(SNLM)。其次,针对明显包含纹理和方向的图像块,提出了一种方向增强邻域窗,使得邻域窗内主导方向像素点在相似度计算中权重增加。实验结果证明,新方法在自然图像去噪中优于传统的NLM算法。特别地,对于纹理图像去噪,基于方向增强邻域窗的算法,能够在去除噪声的同时很好地保留纹理边缘等细节信息。

Non-Local Means（NLM） filter is an effective method for image denoising.However,it only focuses on the geometry structure of image,ignoring the appearance model and directional information.In this paper,a new Non-Subsampled Shearlet Descriptor（NSSD） is proposed and employed to model the appearance of image patches and the measurement of similarity between two image patches is more robust.According to NSSD,a more effective Shearlet Non-Local Means（SNLM） algorithm is proposed by combining the NSSD with non-local computation model.For another,for texture images with directional information,a direction enhance window is proposed,which increases the weights on the main direction in the neighborhood window in the measurement of similarity.Experiment results show that the proposed NLM algorithm gets better performance on natural image denoising than the traditional NLM algorithm.Moreover,for texture image,the algorithm based on direction enhance neighborhood window can not only remove the noise but also preserve the detail information such as edges and textures and show great advantages on denoising.