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A neural network algorithm for fast blind image restoration using a novel 2D-ARMA parameter estimation

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2 Author(s)
Deng Zhipo ; Coll. of Math. & Comput. Sci., Fuzhou Univ., Fuzhou, China ; Xia Youshen

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.

Published in:

Audio Language and Image Processing (ICALIP), 2010 International Conference on

Date of Conference:

23-25 Nov. 2010