  In this paper, an algorithm is proposed for Image Restoration. Such algorithm
is different from the traditional approaches in this area, by utilizing priors
that are learned from similar images. Original images and their degraded
versions by the known degradation operators are utilized for designing the
Quantization. The code vectors are designed using the blurred images. For each
such vector, the high frequency information obtained from the original images
is also available. During restoration, the high frequency information of a
given degraded image is estimated from its low frequency information based on
the artificial noise. For the restoration problem, a number of techniques are
designed corresponding to various versions of the blurring function. Given a
noisy and blurred image, one of the techniques is chosen based on a similarity
measure, therefore providing the identification of the blur. To make the
restoration process computationally efficient, the Quantization Nearest
Neighborhood approaches are utilized.
