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The Scientific World Journal
Volume 2013, Article ID 438147, 10 pages
Research Article

Video Denoising Based on a Spatiotemporal Kalman-Bilateral Mixture Model

College of Information System and Management, National University of Defense Technology, Changsha, Hunan 410073, China

Received 19 August 2013; Accepted 16 September 2013

Academic Editors: W. Su and J. Tian

Copyright © 2013 Chenglin Zuo et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.