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Importance sampling Kalman filter for image estimation
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DOI：10.1109/LSP.2006.891345.png)
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摘要
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This paper presents discontinuity adaptive image estimation within the Kalman filter framework by non-Gaussian modeling of the image prior. A generalized methodology is proposed for specifying state-dynamics using the conditional density of the state given its neighbors, without explicitly defining the state equation. The novelty of our approach lies in directly obtaining the predicted mean and variance of the non-Gaussian state conditional density by importance sampling and incorporating them in the update step of the Kalman filter. Experimental results are given to demonstrate the effectiveness of the proposed method in preserving edges.
Keyword:
discontinuity adaptive prior
image estimation
importance sampling
Kalman filter
Markov random fields
non-Gaussian image modelling
state space models
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9.6
论文数:
1.1W
被引数:
1.7W
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