Nonlinear image processing and filtering: A unified approach based on vertically weighted regression

• Autorzy: Ewaryst Rafajłowicz , Mirosław Pawlak , Angsar Steland
• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

image filtering; vertically weighted regression; nonlinear filters

• Wydawca: University of Zielona Gora Press
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2008
• Tom: 18
• Numer: 1
• Strony: 49-61

Daty

wydano

2008

Twórcy

autor

Ewaryst Rafajłowicz

• Institute of Computer Engineering, Control and Robotics, Wrocław University of Technology, ul. Wybrzeże Wyspiańskiego 27, 50-370 Wrocław, Poland

autor

Mirosław Pawlak

• Institute of Computer Engineering, Control and Robotics, Wrocław University of Technology, ul. Wybrzeże Wyspiańskiego 27, 50-370 Wrocław, Poland

autor

Angsar Steland

• Department of Electrical and Computer Engineering, University of Manitoba, Winnipeg, Manitoba, R3T5V6, Canada
• RWTH Aachen University, Aachen, Germany

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Identyfikatory

DOI

10.2478/v10006-008-0005-z