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

Rafajłowicz, Ewaryst; Pawlak, Mirosław; Steland, Angsar

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

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AMCS, volume 18 (2008)

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.

Publisher:

Zielona Góra: Uniwersytet Zielonogórski

Resource Identifier:

oai:zbc.uz.zgora.pl:78696

DOI:

10.2478/v10006-008-0005-z

Pages:

49-61

Source:

AMCS, volume 18, number 1 (2008) ; click here to follow the link

Language:

eng

Rights:

Biblioteka Uniwersytetu Zielonogórskiego

Subject and keywords:

image filtering vertically weighted regression nonlinear filters

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Repository > Faculties > Faculty of Computer, Electrical and Control Engineering

Repository > Types of work > Articles

Repository > Scientific journals and UZ publishing series > International Journal of Applied Mathematics and Computer Science (AMCS) > International Journal of Applied Mathematics and Computer Science (AMCS) (2008)

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Apr 8, 2024

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Apr 4, 2024

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https://zbc.uz.zgora.pl/repozytorium/publication/88440

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International Journal of Applied Mathematics and Computer Science (AMCS)

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Nonlinear image processing and filtering: A unified approach based on vertically weighted regression	Apr 8, 2024

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