摘要：

针对双边滤波器灰度相似权函数易受噪声影响且在图像细节区域滤波存在盲目性的问题,提出一种新的图像三边滤波器用以过滤高斯噪声。通过局部结构张量奇异值分解估计图像的几何结构信息,获得能够刻画图像内容差异的特征信息。在此基础上,设计基于图像特征分类的灰度相似权函数,同时通过引入结构相似权的方式将鲁棒的特征信息耦合到双边滤波器框架下,以保持更多的图像细节。利用三边加权提供更可靠的像素相似性度量方式,并采用局部自适应滤波参数选取方法进一步提高算法的滤波性能。实验结果表明,该滤波器在去除噪声的同时能够较好地保持图像的边缘、纹理等结构信息。

关键词: 图像去噪, 三边滤波, 局部结构张量, 奇异值分解, 图像结构特征, 相似性度量

Abstract:

The intensity similarity weight function adopted in bilateral filter is subjected to the influence of image noise.Moreover,this filter causes blindness in dealing with image details.To overcome the above drawbacks,this paper proposes a novel trilateral filter for filtering Gaussian noise.It uses Singular Value Decomposition(SVD) to estimate the geometry structure information of the image,and constructs feature information which can describe the difference of image contents.On this basis,it designs intensity similarity weight function based on the image feature classification and incorporates the structure feature into the bilateral filter framework by introducing the structure similarity weight to preserve more image details.It uses the trilateral weighting approach to provide more reliable similarity measurement between the target pixel and its neighbors.Finally,it uses the local adaptive filtering parameter choosing method for better performance.Experimental results show that the proposed filter can obtain better filtering results when preserving image edges and textures well.

Key words: image denoising, trilateral filtering, local structure tensor, Singular Value Decomposition(SVD), image structure feature, similarity measurement

中图分类号: 

• TP391