Abstract
Based on a novel two-dimensional autoregressive moving average (2D-ARMA) parameter estimate, this paper develops a neural network algorithm for fast blind image restoration. The point spread function of degraded image is reformulated as an optimal solution of a quadratic convex programming problem and it is well solved by a neural network. Compared with existing ARMA parametric methods, the proposed approach can overcome the local minimization problem. Unlike iterative blind deconvolution algorithms, the proposed blind image restoration algorithm has a faster blind image restoration. Computed results shows that the proposed algorithm can obtain a better image estimate with a faster speed than two standing blind image restoration algorithms.
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