A Neural Network Algorithm for Fast Blind Image Restoration Using A Novel 2D-ARMA Parameter Estimation
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.
Deng Zhipo Xia Youshen
College of Mathematics and Computer Science Fuzhou University, Fuzhou, China
国际会议
上海
英文
1744-1748
2010-10-20（万方平台首次上网日期，不代表论文的发表时间）