Spatiotemporal Video Denoising Based on Adaptive Thresholding and Clustering
In this paper we propose a novel video denoising method based on adaptive thresholding and K-means clustering. In the proposed method the adaptive thresholding is applied rather than the conventional hard-thresholding of the VBM3D method. The adaptive thresholding has a high ability to adapt and cha...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2017-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2017/7094758 |
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| _version_ | 1832564300124782592 |
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| author | Ali Abdullah Yahya Jieqing Tan Benyu Su Kui Liu Ali Naser Hadi |
| author_facet | Ali Abdullah Yahya Jieqing Tan Benyu Su Kui Liu Ali Naser Hadi |
| author_sort | Ali Abdullah Yahya |
| collection | DOAJ |
| description | In this paper we propose a novel video denoising method based on adaptive thresholding and K-means clustering. In the proposed method the adaptive thresholding is applied rather than the conventional hard-thresholding of the VBM3D method. The adaptive thresholding has a high ability to adapt and change according to the amount of noise. More specifically, hard-thresholding is applied on the higher noise areas while soft-thresholding is applied on the lower noise areas. Consequently, we can successfully remove the noise effectively and at the same time preserve the edges of the image, because the clustering approach saves more computation time and is more capable of finding relevant patches than the block-matching approach. So, the K-means clustering method in the final estimate in this paper is adopted instead of the block-matching method in the VBM3D method in order to restrict the search of the candidate patches within the region of the reference patch and therefore improve the grouping. Experimental results emphasize the superiority of the new method over the reference methods in terms of visual quality, Peak Signal-to-Noise Ratio (PSNR), and Image Enhancement Factor (IEF). Execution time of the proposed algorithm consumes less time in denoising than that in the VBM3D algorithm. |
| format | Article |
| id | doaj-art-ed2cc73d85654a9b833e9aa529f0f900 |
| institution | Kabale University |
| issn | 1026-0226 1607-887X |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-ed2cc73d85654a9b833e9aa529f0f9002025-02-03T01:11:22ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2017-01-01201710.1155/2017/70947587094758Spatiotemporal Video Denoising Based on Adaptive Thresholding and ClusteringAli Abdullah Yahya0Jieqing Tan1Benyu Su2Kui Liu3Ali Naser Hadi4School of Computer and Information, Anqing Normal University, Anqing 246011, ChinaSchool of Computer and Information, Hefei University of Technology, Hefei 230009, ChinaSchool of Computer and Information, Anqing Normal University, Anqing 246011, ChinaSchool of Computer and Information, Anqing Normal University, Anqing 246011, ChinaSchool of Computer and Information, Hefei University of Technology, Hefei 230009, ChinaIn this paper we propose a novel video denoising method based on adaptive thresholding and K-means clustering. In the proposed method the adaptive thresholding is applied rather than the conventional hard-thresholding of the VBM3D method. The adaptive thresholding has a high ability to adapt and change according to the amount of noise. More specifically, hard-thresholding is applied on the higher noise areas while soft-thresholding is applied on the lower noise areas. Consequently, we can successfully remove the noise effectively and at the same time preserve the edges of the image, because the clustering approach saves more computation time and is more capable of finding relevant patches than the block-matching approach. So, the K-means clustering method in the final estimate in this paper is adopted instead of the block-matching method in the VBM3D method in order to restrict the search of the candidate patches within the region of the reference patch and therefore improve the grouping. Experimental results emphasize the superiority of the new method over the reference methods in terms of visual quality, Peak Signal-to-Noise Ratio (PSNR), and Image Enhancement Factor (IEF). Execution time of the proposed algorithm consumes less time in denoising than that in the VBM3D algorithm.http://dx.doi.org/10.1155/2017/7094758 |
| spellingShingle | Ali Abdullah Yahya Jieqing Tan Benyu Su Kui Liu Ali Naser Hadi Spatiotemporal Video Denoising Based on Adaptive Thresholding and Clustering Discrete Dynamics in Nature and Society |
| title | Spatiotemporal Video Denoising Based on Adaptive Thresholding and Clustering |
| title_full | Spatiotemporal Video Denoising Based on Adaptive Thresholding and Clustering |
| title_fullStr | Spatiotemporal Video Denoising Based on Adaptive Thresholding and Clustering |
| title_full_unstemmed | Spatiotemporal Video Denoising Based on Adaptive Thresholding and Clustering |
| title_short | Spatiotemporal Video Denoising Based on Adaptive Thresholding and Clustering |
| title_sort | spatiotemporal video denoising based on adaptive thresholding and clustering |
| url | http://dx.doi.org/10.1155/2017/7094758 |
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