Recursive Image Enhancement--Vector Processing

A new approach to design of a recursive image enhancer is introduced when the image is characterized statistically by its mean and correlation function. A vector linear dynamical model is derived to represent the statistics of the processor output when several lines of the picture are processed simultaneously. Based on the vector model, a Kalman filter is designed and utilized to recursively enhance the image. The vector processing results in a simpler and more accurate image enhancement algorithm in comparison with scalar processing. Two examples, one with very low signal-to-noise ratio, are used to illustrate the effectiveness of the procedure. Finally, the performance of the vector and scalar estimators is compared.