结合邻域方差和各向异性窗的引导滤波算法

Guided Filtering Combining Neighborhood Variance and Anisotropic Window

针对引导滤波会导致边缘附近出现光晕且难以识别精细边缘的问题,提出了一种结合邻域方差与各向异性窗的引导滤波算法.首先,利用各向异性高斯滤波器的方向选择性实现对边缘的精细识别,并利用滤波器的狭长空域结构可实现局部窗口内不同像素信息融合,以抑制边缘模糊和光晕效果;其次,基于局部结构相似性原理,引入邻域方差以实现对局部线性变换参数的优化,同时保证强边缘结构和非边缘区域的最大扩散.实验结果表明,在102类花卉图像数据集上,文中算法的视觉效果、定量评价(PSNR和SSIM)均优于其他边缘保持滤波算法,并且测试图像的失真度比引导滤波、加权引导滤波和各向异性引导滤波分别小46.72%,48.64%和29.61%,能够在识别精细边缘的同时有效地抑制伪影现象的发生.

Aiming at the problem that guided filtering would cause halo near the edge and it is difficult to identify fine edges,a guided filtering combining neighborhood variance and anisotropic window is proposed.Firstly,the directional selectivity of the anisotropic Gaussian filter is used for the recognition of the fine edge,and the narrow spatial structure of the filter can benefit the information fusion of different pixels inside the local window to suppress the edge blur and halo effect.Secondly,based on the local structure similarity,the neighborhood variance is introduced to optimize the parameters of local linear transformation,so as to achieve the maximum diffusion at non-edge region while preserving strong edges.The experimental results on 102 Category Flower Dataset show that,compared with other edge-preserving filtering methods,the proposed method is superior to other methods in visual effect and quantitative evaluation(PSNR and SSIM),and the distortion of the test image is 46.72%,48.64% and 29.61% smaller than guided filtering,weighted guided filtering and anisotropic guided filtering respectively.It can effectively suppress the occurrence of artifacts while recognizing precise edges.