A motion-compensated spatio-temporal filter for image sequences with signal-dependent noise

A novel spatio-temporal filter is described for monochrome image sequences with either signal-independent or signal-dependent noise by considering both spatial and temporal correlations. With the assumptions of spatio-temporal separability and temporal stationarity, it is shown that motion-compensated groups of frames can be decorrelated by using the Karhunen-Loeve transform. Practical filters that work well on a variety of image sequences are developed by first applying the Hadamard transform along the temporal direction. Subsequently, the parametric adaptive Wiener filter is applied to each of the resulting approximately decorrelated transformed images. These transformed images are classified into one average image and a remaining set of residual images, which provide interesting and useful interpretations of the type of image sequence. The filter performance is evaluated by considering different types of image sequences in the database. The procedure advanced for processing a sequence of monochrome images can be adapted for generalization to multispectral images and this possibility is currently under detailed investigation.