A Kalman Filter based Video Denoising Method Using Intensity and Structure Tensor

• Yu Liu,
• Chenlin Zuo,
• Xin Tan,
• Huaxin Xiao,
• Maojun Zhang,

Vol. 8, No. 8, August 28, 2014
10.3837/tiis.2014.08.017, Download Paper (Free):

• Video denoising
• Kalman filter
• motion estimation
• structure tensor
• Wiener filter

Abstract

We propose a video denoising method based on Kalman filter to reduce the noise in video sequences. Firstly, with the strong spatiotemporal correlations of neighboring frames, motion estimation is performed on video frames consisting of previous denoised frames and current noisy frame based on intensity and structure tensor. The current noisy frame is processed in temporal domain by using motion estimation result as the parameter in the Kalman filter, while it is also processed in spatial domain using the Wiener filter. Finally, by weighting the denoised frames from the Kalman and the Wiener filtering, a satisfactory result can be obtained. 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.

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