Guided Filtering Combining Neighborhood Variance and Anisotropic Window

Abstract

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.