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Improving Global Neighborhood Structure Map Denoising Approach for Digital Images

Hossain, MD Moinul (2019) Improving Global Neighborhood Structure Map Denoising Approach for Digital Images. In: 13th International Conference on Interfaces and Human Computer Interaction. . pp. 207-214. iadis digital library ISBN 978-989-8533-91-3. (KAR id:73724)

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Abstract

This paper proposes a new noise reduction model for digital images. In the proposed model, the intensity similarity between the center pixel and its neighboring pixels within a certain window for constructing a Global Neighborhood Structure (GNS) using Dominant Neighborhood Structure (DNS) maps of central pixels has been measured. The intensity similarity was calculated by using the Canberra Distance measurement equation; where the conventional GNS map approach used the Euclidean distance principle. To evaluate the performance of the proposed model, several noise attacks were imposed on two public image datasets and experimental results demonstrated that the proposed model outperforms the conventional GNS map based denoising technique by exhibiting higher PSNR and SNR values.

Item Type: Conference or workshop item (Proceeding)
Subjects: Q Science
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts
Depositing User: Moinul Hossain
Date Deposited: 01 May 2019 16:45 UTC
Last Modified: 16 Feb 2021 14:04 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/73724 (The current URI for this page, for reference purposes)
Hossain, MD Moinul: https://orcid.org/0000-0003-4184-2397
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