Video Noise Reduction Method Using Adaptive Spatial-Temporal Filtering

We proposed a novel method of video noise reduction based on the spatial Wiener filter and the temporal filter. In the proposed spatial Wiener filter, both the amount of noise and the size of the mask are taken into consideration. The proposed model has a great capacity to be adaptive in each area i...

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Main Authors: Ali Abdullah Yahya, Jieqing Tan, Lian Li
Format: Article
Language:English
Published: Wiley 2015-01-01
Series:Discrete Dynamics in Nature and Society
Online Access:http://dx.doi.org/10.1155/2015/351763
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author Ali Abdullah Yahya
Jieqing Tan
Lian Li
author_facet Ali Abdullah Yahya
Jieqing Tan
Lian Li
author_sort Ali Abdullah Yahya
collection DOAJ
description We proposed a novel method of video noise reduction based on the spatial Wiener filter and the temporal filter. In the proposed spatial Wiener filter, both the amount of noise and the size of the mask are taken into consideration. The proposed model has a great capacity to be adaptive in each area in accordance with the amount of noise. In the proposed model, the motion detector is applied to control the noise removal process in accordance with the area’s information (i.e., static or movable). More accurately, more noise removal is done in the areas that are potentially still areas and less removal in the areas that are potentially motion areas. The proposed model achieves a maximum gain of 7.6 dB and capacity of conserving the significant image features (e.g., edges). The experimental results demonstrate that the new approach is more efficient than reference methods in terms of noise removal and edges preservation.
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institution Kabale University
issn 1026-0226
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language English
publishDate 2015-01-01
publisher Wiley
record_format Article
series Discrete Dynamics in Nature and Society
spelling doaj-art-7741ddbff47e4c9cb185ee4d96212fc82025-02-03T01:21:58ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2015-01-01201510.1155/2015/351763351763Video Noise Reduction Method Using Adaptive Spatial-Temporal FilteringAli Abdullah Yahya0Jieqing Tan1Lian Li2 School of Computer and Information, Hefei University of Technology, Hefei 230009, China School of Computer and Information, Hefei University of Technology, Hefei 230009, China School of Computer and Information, Hefei University of Technology, Hefei 230009, ChinaWe proposed a novel method of video noise reduction based on the spatial Wiener filter and the temporal filter. In the proposed spatial Wiener filter, both the amount of noise and the size of the mask are taken into consideration. The proposed model has a great capacity to be adaptive in each area in accordance with the amount of noise. In the proposed model, the motion detector is applied to control the noise removal process in accordance with the area’s information (i.e., static or movable). More accurately, more noise removal is done in the areas that are potentially still areas and less removal in the areas that are potentially motion areas. The proposed model achieves a maximum gain of 7.6 dB and capacity of conserving the significant image features (e.g., edges). The experimental results demonstrate that the new approach is more efficient than reference methods in terms of noise removal and edges preservation.http://dx.doi.org/10.1155/2015/351763
spellingShingle Ali Abdullah Yahya
Jieqing Tan
Lian Li
Video Noise Reduction Method Using Adaptive Spatial-Temporal Filtering
Discrete Dynamics in Nature and Society
title Video Noise Reduction Method Using Adaptive Spatial-Temporal Filtering
title_full Video Noise Reduction Method Using Adaptive Spatial-Temporal Filtering
title_fullStr Video Noise Reduction Method Using Adaptive Spatial-Temporal Filtering
title_full_unstemmed Video Noise Reduction Method Using Adaptive Spatial-Temporal Filtering
title_short Video Noise Reduction Method Using Adaptive Spatial-Temporal Filtering
title_sort video noise reduction method using adaptive spatial temporal filtering
url http://dx.doi.org/10.1155/2015/351763
work_keys_str_mv AT aliabdullahyahya videonoisereductionmethodusingadaptivespatialtemporalfiltering
AT jieqingtan videonoisereductionmethodusingadaptivespatialtemporalfiltering
AT lianli videonoisereductionmethodusingadaptivespatialtemporalfiltering