A VQ-based blur identification algorithm

The estimation of the point spread function (PSF) of the degradation system is often a necessary first step in the restoration of blurred images. In this work, a novel Vector Quantization (VQ)-based blur identification algorithm is presented. A number of codebooks are designed corresponding to various versions of the blurring function. Prototype images blurred by each candidate blur are used. Only the non-flat regions for specific frequency bands are represented by the entries of the codebooks. Given a noisy and blurred image, one of the codebooks is chosen based on a similarity measure, therefore providing the identification of the blur. Simulations are performed for various blurring functions and noise levels. The results demonstrate the effectiveness of the proposed algorithms.