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

fYear

1996

Firstpage

222

Lastpage

227

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;

fLanguage

English

Publisher

ieee

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

Type

conf

DOI

10.1109/NICRSP.1996.542763

Filename

542763

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1718087