Multiple model recursive estimation of images
In this paper, we demonstrate the application of the reduced update Kalman filter in the enhancement of two-dimensional images using a composite model description of the image. Typically, for the purpose of simulation, five models corresponding to four predominant correlation directions (at angles of 0°, 45°, 90°, 135° to the horizontal) and one isotropic model, are considered. These models are then used to synthesize a filtering algorithm that estimates the image with near minimum mean square error. The results show considerable improvement in the visual quality compared with linear constant coefficient Kalman filtering.