Image restoration using a reduced order model Kalman filter

The one-dimensional state-space representation of an image for restoration using a Kalman filter requires a relatively large state vector. For a typical autoregressive signal model with nonsymmetric half-plane support, the dimension of the state is approximately equal to the product of the image model order and the pixel width of the image. This state is large, particularly for practical images and would require excessive computation if the Kalman filter were used directly. Consequently, there have been various filtering approximations which reduce the amount of computation. An alternate approach is to reduce the dimension of the image model and use the corresponding optimal filter directly. To this effect a reduced-order model Kalman filter which reduces both the amount and the complexity of the computations is developed