An optimal neuron evolution algorithm for constrained quadratic programming in image restoration

An optimal neuron evolution algorithm for the restoration of linearly distorted images is presented in this paper. The proposed algorithm is motivated by the symmetric positive-definite quadratic programming structure inherent in restoration. Theoretical analysis and experimental results show that the algorithm not only significantly increases the convergence rate of processing, but also produces good restoration results. In addition, the algorithm provides a genuine parallel processing structure which ensures computationally feasible spatial domain image restoration.

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