Image Restoration With Static Gaps on the Basis of Association Rules

Белим, С. В.; Mayorov-Zilbernagel, Andrey

doi:10.14489/vkit.2014.12.pp.018-023

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vkit

2014

DOI: 10.14489/vkit.2014.12.pp.018-023

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Image Restoration With Static Gaps on the Basis of Association Rules

С. В. Белим¹,

Andrey Mayorov-Zilbernagel²

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2017

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The algorithm of the impulse noise filtration in images based on an algorithm of community detection in graphs

Белим

Larionov

2017

2017 Dynamics of Systems, Mechanisms and Machines (Dynamics)

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This article suggests an algorithm of impulse noise filtration, based on the community detection in graphs. The image is representing as non-oriented weighted graph. Each pixel of an image is corresponding to a vertex of the graph. Community detection algorithm is running on the given graph. Assumed that communities that contain only one pixel are corresponding to noised pixels of an image. Suggested method was tested with help of computer experiment.This experiment was conducted on grayscale, and on colored images, on artificial images and on photos. It is shown that the suggested method is better than median filter by 20% regardless of noise percent. Higher efficiency is justified by the fact that most of filters are changing all of image pixels, but suggested method is finding and restoring only noised pixels. The dependence of the effectiveness of the proposed method on the percentage of noise in the image is shown.

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The algorithm of the impulse noise filtration in images based on an algorithm of community detection in graphs

Белим

Larionov

2017

2017 Dynamics of Systems, Mechanisms and Machines (Dynamics)

Self Cite

View full text Add to dashboard Cite

show abstract

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Image Restoration With Static Gaps on the Basis of Association Rules

Cited by 1 publication

References 0 publications

The algorithm of the impulse noise filtration in images based on an algorithm of community detection in graphs

The algorithm of the impulse noise filtration in images based on an algorithm of community detection in graphs

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