title: Edge-Aware Filtering with Local Polynomial Approximation and Rectangle-Based Weighting.
abstract: This paper presents a novel method for performing guided image filtering using local polynomial approximation (LPA) with range guidance. In our method, the LPA is introduced into a multipoint framework for reliable model regression and better preservation on image spatial variation which usually contains the essential information in the input image. In addition, we develop a weighting scheme which has the spatial flexibility during the filtering process. All components in our method are efficiently implemented and a constant computation complexity is achieved. Compared with conventional filtering methods, our method provides clearer boundaries and performs especially better in recovering spatial variation from noisy images. We conduct a number of experiments for different applications: depth image upsampling, joint image denoising, details enhancement, and image abstraction. Both quantitative and qualitative comparisons demonstrate that our method outperforms state-of-the-art methods.
authors.forename: Xiao
authors.initials: X
authors.lastname: Tan
authors.forename: Changming
authors.initials: C
authors.lastname: Sun
authors.forename: Tuan D
authors.initials: TD
authors.lastname: Pham