Nonlinear Image Processing and Filtering: A Unified Approach Based on Vertically Weighted Regression

Ewaryst Rafajłowicz 1 , Mirosław Pawlak 1  and Angsar Steland
  • 1 Institute of Computer Engineering, Control and Robotics, Wrocław University of Technology, ul. Wybrzeże Wyspiańskiego 27, 50-370 Wrocław, Poland
  • 2 Department of Electrical and Computer Engineering, University of Manitoba, Winnipeg, Manitoba, R3T5V6, Canada
  • 3 RWTH Aachen University Aachen, Germany

Nonlinear Image Processing and Filtering: A Unified Approach Based on Vertically Weighted Regression

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.

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