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Title:
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IMPROVING GLOBAL NEIGHBORHOOD STRUCTURE
MAP DENOISING APPROACH FOR DIGITAL IMAGES |
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Author(s):
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Wasif Shafaet Chowdhury, Jia Uddin, Hamed Alsufyani and Md. Moinul Hossain |
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ISBN:
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978-989-8533-91-3 |
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Editors:
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Katherine Blashki and Yingcai Xiao |
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Year:
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2019 |
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Edition:
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Single |
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Keywords:
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Dominant Neighborhood Structure (DNS), Noise Reduction, Canberra Distance Measurement Equation |
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Type:
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Full Paper |
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First Page:
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207 |
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Last Page:
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214 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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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. |
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