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Neural least-squares image filter with positive constraints

In this paper a novel neural approach for restoration of grey images degraded by known blur function and additive noise is presented. The neuron-like optimizer solver is used for constrained least-squares image filtering with positive constraints. The solution is based on penalty function methods for nonlinear, optimization problems. Continuous and discrete filter realizations are described. The practical examples is given.