Design of regularization filters with linear neural networks

Taek M Kwon, Michael E. Zervakis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

In this paper we propose a linear neural network (LNN) that is suitable for the implementation of least squares (LS) and regularized inversion problems. We apply this network to the design of regularized filters, which are commonly used in image restoration problems. The constrained least squares (CLS) filter and the robust CLS regularized filter are considered. The CLS regularized filter is implemented using the proposed LNN, whereas the robust CLS regularized filter is implemented using a nonlinear modification called quasi-LNN (Q-LNN). Several examples of actual image restoration applications are presented, which are based on the simulation of the proposed filters. SPICE simulation results of an actual circuit is also presented.

Original languageEnglish (US)
Title of host publication1992 IEEE International Conference on Systems, Man, and Cybernetics
Subtitle of host publicationEmergent Innovations in Information Transfer Processing and Decision Making, SMC 1992
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages416-421
Number of pages6
ISBN (Electronic)0780307208, 9780780307209
DOIs
StatePublished - Jan 1 1992
EventIEEE International Conference on Systems, Man, and Cybernetics, SMC 1992 - Chicago, United States
Duration: Oct 18 1992Oct 21 1992

Publication series

NameConference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
Volume1992-January
ISSN (Print)1062-922X

Other

OtherIEEE International Conference on Systems, Man, and Cybernetics, SMC 1992
Country/TerritoryUnited States
CityChicago
Period10/18/9210/21/92

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