title: Video denoising based on a spatiotemporal Kalman-bilateral mixture model.
abstract: 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.
authors.affiliation: College of Information System and Management, National University of Defense Technology, Changsha, Hunan 410073, China.
authors.forename: Chenglin
authors.initials: C
authors.lastname: Zuo
authors.forename: Yu
authors.initials: Y
authors.lastname: Liu
authors.forename: Xin
authors.initials: X
authors.lastname: Tan
authors.forename: Wei
authors.initials: W
authors.lastname: Wang
authors.forename: Maojun
authors.initials: M
authors.lastname: Zhang
mesh_terms: D016477:Artifacts; D007089:Image Enhancement / Q000379:methods*; D008962:Models, Theoretical; D059629:Signal-To-Noise Ratio; D014741:Video Recording / Q000379:methods*