Importance Sampling Kalman Filter for Image Estimation

G. R. K. S. Subrahmanyam, A. N. Rajagopalan, R. Aravind
2007 IEEE Signal Processing Letters  
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
more » ... n the update step of the Kalman filter. Experimental results are given to demonstrate the effectiveness of the proposed method in preserving edges. Index Terms-Discontinuity adaptive prior, image estimation, importance sampling, Kalman filter, Markov random fields, non-Gaussian image modelling, state space models.
doi:10.1109/lsp.2006.891345 fatcat:6ilg5rbauvbuvgdz3ytmzwuuw4