Adaptive image sensing and enhancement using the cellular neural network universal machine

Mátyás Brendel, T. Roska

Research output: Article

9 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)287-312
Number of pages26
JournalInternational Journal of Circuit Theory and Applications
Volume30
Issue number2-3
DOIs
Publication statusPublished - márc. 2002

Fingerprint

Cellular neural networks
Image enhancement
Cellular Networks
Sensing
Enhancement
Neural Networks
Image Enhancement
Chip
Dependent
Computing
Image processing
Simulators
Interactive Methods
Cell Complex
Sensors
Adaptive Control
Multilayer
Image Processing
Simulator
Experiments

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Cite this

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abstract = "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.",
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author = "M{\'a}ty{\'a}s Brendel and T. Roska",
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