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Importance sampling Kalman filter for image estimation
Date Issued
01-07-2007
Author(s)
Subrahmanyam, G. R.K.S.
Indian Institute of Technology, Madras
Indian Institute of Technology, Madras
Abstract
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. © 2007 IEEE.
Volume
14