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

Mátyás Brendel, Tamás Roska

Research output: Contribution to journalArticle

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 - Mar 1 2002

Keywords

  • Adaptive image enhancement
  • Adaptive sensing
  • CNN

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics

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