Estimation and identification of two-dimensional images

Abstract

Estimation of image pixel density can be performed using a reduced update Kalman filter provided that a mathematical model for the image generating process is available. To this effect various algorithms suitable for identifying the parameters of autoregressive image models are discussed and evaluated in terms of the mean-squared error between the true and filtered images. Algorithms considered include general and bias-compensated least-square procedures, a correlation-based algorithm, and procedures involving the simultaneous estimation of both the image model coefficient vector and pixel estimates. Experiments using two real images and two random fields indicate that bias-compensated least squares and correlation-based procedures might be most useful for image identification and adaptive filtering.