The Scientific World Journal (Jan 2013)

Video Denoising Based on a Spatiotemporal Kalman-Bilateral Mixture Model

  • Chenglin Zuo,
  • Yu Liu,
  • Xin Tan,
  • Wei Wang,
  • Maojun Zhang

DOI
https://doi.org/10.1155/2013/438147
Journal volume & issue
Vol. 2013

Abstract

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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.