Weighted Adaptive Guided Filtering Based on Kurtosis Variance

To overcome the difficulty in extracting edge of noise image with traditional local window variance, a kurtosis coefficient variance was introduced to percept edges of noise image. Meanwhile, a novel weighted adaptive guiding filter (WAGIF) was proposed. Firstly, the fixed regularization parameter of traditional guiding filter was adjusted adaptively based on edge information of local window. Secondly, the weighted aggregation was achieved based on the multi-neighbor window edge weight. And then edge blurring was further suppressed. Experimental results show that the average PSNR and SSIM on different noise levels are boosted above 65% and 78% respectively using WAGIF. Filtering results show better performance in noise smoothing and edge-preserving according to state-of-the-art image filters.