A multiple model algorithm for the adaptive restoration of images
We present a multiple model image restoration technique with on-line edge detection over noisy and blurred images. Four edge models corresponding to major correlation directions and an isotropic model are used to represent the image. Unlike previous results, a decision-directed approach is used to adaptively estimate the edge orientation which then defines the appropriate deconvolution model. The overall effect is space-variant deconvolution implemented by efficient reduced update filters. Processed images are shown as examples.