Noise reduction in images using statistical filtering

This paper deals with noise reduction in images using two-dimensional Kalman filtering. To reduce the computational load and the memory storage, updating of state vector is done within a certain distance of the point currently being processed, the support of the filter and dynamical model is restricted to a non-symmetric half plane. Direct histogram specification method is combined with Kalman filtering algorithm. Significant improvement in the quality of the image is obtained. The technique used is particularly attractive for on-line applications. It is also shown that the various gradient computations and template matching schemes work efficiently on estimated image to extract vital features (edges) from the image for the future analysis and studies. The results obtained confirm the feasibility of these algorithms.