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Video Denoising Based on a Spatiotemporal Kalman-Bilateral Mixture Model
Joint Authors
Liu, Yu
Wang, Wei
Zhang, Maojun
Zuo, Chenglin
Tan, Xin
Source
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-11-03
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
We propose a video denoising method based on a spatiotemporal Kalman-bilateral mixture model to reduce the noise in video sequences that are captured with low light.
To take full advantage of the strong spatiotemporal correlations of neighboring frames, motion estimation is first performed on video frames consisting of previously denoised frames and the current noisy frame by using block-matching method.
Then, current noisy frame is processed in temporal domain and spatial domain by using Kalman filter and bilateral filter, respectively.
Finally, by weighting the denoised frames from Kalman filtering and bilateral filtering, we can obtain a satisfactory result.
Experimental results show that the performance of our proposed method is competitive when compared with state-of-the-art video denoising algorithms based on both peak signal-to-noise-ratio and structural similarity evaluations.
American Psychological Association (APA)
Zuo, Chenglin& Liu, Yu& Tan, Xin& Wang, Wei& Zhang, Maojun. 2013. Video Denoising Based on a Spatiotemporal Kalman-Bilateral Mixture Model. The Scientific World Journal،Vol. 2013, no. 2013, pp.1-10.
https://search.emarefa.net/detail/BIM-1032924
Modern Language Association (MLA)
Zuo, Chenglin…[et al.]. Video Denoising Based on a Spatiotemporal Kalman-Bilateral Mixture Model. The Scientific World Journal No. 2013 (2013), pp.1-10.
https://search.emarefa.net/detail/BIM-1032924
American Medical Association (AMA)
Zuo, Chenglin& Liu, Yu& Tan, Xin& Wang, Wei& Zhang, Maojun. Video Denoising Based on a Spatiotemporal Kalman-Bilateral Mixture Model. The Scientific World Journal. 2013. Vol. 2013, no. 2013, pp.1-10.
https://search.emarefa.net/detail/BIM-1032924
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1032924