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CVPR
2007
IEEE

Spatio-Temporal Markov Random Field for Video Denoising

14 years 5 months ago
Spatio-Temporal Markov Random Field for Video Denoising
This paper presents a novel spatio-temporal Markov random field (MRF) for video denoising. Two main issues are addressed in this paper, namely, the estimation of noise model and the proper use of motion estimation in the denoising process. Unlike previous algorithms which estimate the level of noise, our method learns the full noise distribution nonparametrically which serves as the likelihood model in the MRF. Instead of using deterministic motion estimation to align pixels, we set up a temporal likelihood by combining a probabilistic motion field with the learned noise model. The prior of this MRF is modeled by piece-wise smoothness. The main advantage of the proposed spatio-temporal MRF is that it integrates spatial and temporal information adaptively into a statistical inference framework, where the posteriori is optimized using graph cuts with alpha expansion. We demonstrate the performance of the proposed approach on benchmark data sets and real videos to show the advantages of ...
Jia Chen, Chi-Keung Tang
Added 12 Oct 2009
Updated 12 Oct 2009
Type Conference
Year 2007
Where CVPR
Authors Jia Chen, Chi-Keung Tang
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