Possible applications of the Kalman filter and its variants to image enhancement and recovery

Digital image enhancement is a field that incorporates prediction and estimation based on local and global features and the entire image. Artificial intelligent techniques require training based on known information and can be a good tool for image enhancement, however, if the learning process is incomplete or corrupted based on faulty and irrelevant images these techniques can faulter. In this work we investigate a robust predictive technique, the Kalman filter, KF, often used for tracking and guidance applications to evaluate its performance for image enhancement and recovery. To expand our work, we have taken a few liberties with the KF and utilized a Bayesian approach along with a Hidden Markov Model estimator to improve the performance of the KF. Other statistical techniques have also been investigated based on probability density function estimation and its application with the KF to improve the filter’s performance given that the image’s structural information may be utilized for these purposes.