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, hut also produces good restoration

results. In addition, the algorithm provides a genuine parallel processing

structure which ensures computationally feasible spatial domain image

restoration