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Paper details
Number 1 - March 2008
Volume 18 - 2008
Nonlinear image processing and filtering: A unified approach based on vertically weighted regression
Ewaryst Rafajłowicz, Mirosław Pawlak, Angsar Steland
Abstract
A class of nonparametric smoothing kernel methods for image processing and filtering that possess edge-preserving properties is examined. The proposed approach is a nonlinearly modified version of the classical nonparametric regression estimates utilizing the concept of vertical weighting. The method unifies a number of known nonlinear image filtering and denoising algorithms such as bilateral and steering kernel filters. It is shown that vertically weighted filters can be realized by a structure of three interconnected radial basis function (RBF) networks. We also assess the performance of the algorithm by studying industrial images.
Keywords
image filtering, vertically weighted regression, nonlinear filters