鲁棒自适应加权的引导滤波算法  

A Guided Image Filtering Algorithm Based on Robust Adaptive Weighting

作　　者：李喆 李建增 扈琪 LI Zhe;LI Jlan-zeng;HU Qi(Army Engineering University,Shijiazhuang 050003,China;Hebei University,Baoding 071002,China)

机构地区：[1]陆军工程大学,石家庄050003 [2]河北大学,河北保定071002

出　　处：《电光与控制》2019年第1期26-30,共5页Electronics Optics & Control

基　　金：国家自然科学基金(51307183)

摘　　要：为了提高图像滤波时边缘的保持能力,提出鲁棒自适应加权的引导滤波算法。首先利用一阶差分法判断高斯滤波处理后引导图像的边缘位置信息,在去除噪声干扰的同时,提高边缘信息提取的鲁棒性,然后通过最大类间方差法(Otsu)分割边缘区域与非边缘区域,提高区域阈值选取的自适应性,最后利用改进的分段函数模型拟合理想权重因子,控制不同区域的平滑程度,实现鲁棒自适应引导滤波,达到保边平滑的目的。通过图像平滑实验与抠图实验对所提算法性能进行了验证,与引导滤波算法及另外2种改进算法相比,所提算法的保边平滑性能更强。To improve the edge-preserving ability of image filtering a guided image filtering algorithm based on robust adaptive weighting is proposed. Firstly Gaussian filter is performed on the guided image and firstorder differential method is used to judge the position of the edge which can remove the noise as well as improve the robustness of the edge information extraction. Then Otsu is used to segment the edge regions from the non-edge regions the self-adaptability of threshold selection is improved. Finally the improved piecewise function model is used to fit the ideal weight factor and control the degree of smoothness of different regions which can realize the robust adaptive guided filtering and achieve the smoothness of the edge. The experiments of edge-preserving smoothing and matting are carried out. Compared with guided image filtering algorithm and the other two improved algorithms the proposed algorithm has better robustness and edge-preserving smoothness.

关 键 词：图像处理 引导滤波 保边平滑 高斯滤波 最大类间方差法 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]