Image restoration by inhomogeneous G-M field modeling and adaptive Kalman filtering

An inhomogeneous Gauss-Markov (G-M) model of the image field is used to enhance the visual quality of the restored image by Kalman filtering in the spatial domain. In the corresponding Kalman filter, the transition matrix in the predicting part is no longer a constant matrix as it was in previous homogeneous G-M modeling. Instead, it becomes a function of the spatial coordinates as well as the edge running parameter θ. The optimal estimate is a weighted sum of several Kalman filter estimates with each Kalman filter operating in parallel with a separate known value of θ. The experimental results are compared with those obtained in the homogeneous G-M modeling