Title :
Neural least-squares image filter with positive constraints
Author :
Akowski, Jaroslaw Szost ; Niak, Andrzej Staj
Author_Institution :
Inst. of Control & Ind. Electron., Warsaw Univ. of Technol., Poland
Abstract :
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
Keywords :
filtering theory; image restoration; least squares approximations; neural nets; nonlinear programming; additive noise; blur function; continuous filter realizations; discrete filter realizations; grey image restoration; neural least-squares image filter; neuron-like optimizer solver; nonlinear optimization problems; penalty function methods; positive constraints; Additive noise; Constraint optimization; Degradation; Equations; Filtering; Gaussian processes; Image restoration; Industrial electronics; Optimization methods; Wiener filter;
Conference_Titel :
Neural Networks for Identification, Control, Robotics, and Signal/Image Processing, 1996. Proceedings., International Workshop on
Conference_Location :
Venice
Print_ISBN :
0-8186-7456-3
DOI :
10.1109/NICRSP.1996.542763
