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Adaptive image sensing and enhancement using the cellular neural network universal machine
Author(s) -
Brendel Mátyás,
Roska Tamás
Publication year - 2002
Publication title -
international journal of circuit theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.364
H-Index - 52
eISSN - 1097-007X
pISSN - 0098-9886
DOI - 10.1002/cta.201
Subject(s) - computer science , executable , cellular neural network , universal turing machine , key (lock) , artificial neural network , image processing , computer engineering , image (mathematics) , realization (probability) , artificial intelligence , computer architecture , algorithm , turing machine , statistics , computer security , mathematics , computation , operating system
As an attempt to introduce interactive, content‐dependent adaptive (ICDA) image processing, a simple but powerful active image sensing and two image enhancement methods are introduced via adaptive CNN‐UM sensor‐computers. Thus, the method ICDA can be used for adaptive control of image sensing and for subsequent on‐line or off‐line image enhancement as well. The algorithms use both intensity and contrast content. The image sensing technology can be realized with the current CNN‐UM chip. Our first image enhancement method is also executable on this chip, but it is more suitable for the adaptive cellular neural network universal machine (ACNN‐UM) architecture. Some results of simulator and chip experiments and an adaptive extended cell are presented. Our second, dynamical image enhancement method is planned to be executable on a multi‐layer, complex cell CNN architecture. In ( Proceedings of the 6th IEEE International Workshop on Cellular Neural Networks and their Applications ( CNNA‐2000 ) Catania, 2000; 213–217) 3‐layer architecture is described which is capable of realizing the main part of the second enhancement method. The main issues of our paper are as follows: the novel outlook of the ICDA framework, three new methods for two key application areas of CNN‐UM, the notion of ‘regional’ adaptive computing, the novelty of application of equilibrium‐computing in the third method. However, the key novelty of our work is not just a new method and a new realization: by combining sensing and computing, dynamically and pixelwise, a new quality becomes practical. Copyright © 2002 John Wiley & Sons, Ltd.