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 language | English (US) |
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| Title of host publication | 1992 IEEE International Conference on Systems, Man, and Cybernetics |
| Subtitle of host publication | Emergent Innovations in Information Transfer Processing and Decision Making, SMC 1992 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 416-421 |
| Number of pages | 6 |
| ISBN (Electronic) | 0780307208, 9780780307209 |
| DOIs | |
| State | Published - 1992 |
| Event | IEEE International Conference on Systems, Man, and Cybernetics, SMC 1992 - Chicago, United States Duration: Oct 18 1992 → Oct 21 1992 |
Publication series
| Name | Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics |
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| Volume | 1992-January |
| ISSN (Print) | 1062-922X |
Other
| Other | IEEE International Conference on Systems, Man, and Cybernetics, SMC 1992 |
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| Country/Territory | United States |
| City | Chicago |
| Period | 10/18/92 → 10/21/92 |
Bibliographical note
Publisher Copyright:© 1992 IEEE.