Decision-directed segmentation for the restoration of images degraded by a class of space-variant blurs

A decision-directed filtering algorithm has been developed for model-based segmentation and restoration of images degraded by a class of space-variant blurs. It is assumed that the space-variant blur can be represented by a collection of L distinct point-spread functions, where L is a predetermined integer, so that at each pixel one of the functions will be more or less matched to the observed data. A multiple-model Kalman filtering procedure with online model detection based on maximum a posteriori probability decision was used to restore the image. The result of the decision process constitutes a model-based segmentation of the degraded image into regions of spatially invariant blurs. There are several applications of the proposed algorithm. Among these, automatic blur detection, i.e. segmentation of a partially blurred image into regions of blur and no-blur, is demonstrated as an example