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In this paper, an algorithm is proposed for Image Restoration. Such algorithmis different from the traditional approaches in this area, by utilizing priorsthat are learned from similar images. Original images and their degradedversions by the known degradation operators are utilized for designing theQuantization. The code vectors are designed using the blurred images. For eachsuch vector, the high frequency information obtained from the original imagesis also available. During restoration, the high frequency information of agiven degraded image is estimated from its low frequency information based onthe artificial noise. For the restoration problem, a number of techniques aredesigned corresponding to various versions of the blurring function. Given anoisy and blurred image, one of the techniques is chosen based on a similaritymeasure, therefore providing the identification of the blur. To make therestoration process computationally efficient, the Quantization NearestNeighborhood approaches are utilized.

Supervised Learning of Digital image restoration based on Quantization Nearest Neighbor algorithm