Low-level image processing and edge enhancement using a self organizing neural network

Atam P. Dhawan, Thomas Dufresne

Research output: Contribution to conferencePaperpeer-review

6 Scopus citations

Abstract

A self-organizing artificial neural network has been described to enhance and restore gray-level images for applications in low-level image processing. The image is described by a set of interconnected neurons with their values equal to the gray-level values of corresponding pixels. The first-order and second-order contrast links are defined among the neurons which are analyzed for a change in their values in the adaptive constrained environment. Each selected neuron is analyzed only once per iteration, in which its value may be readjusted by incrementing or decrementing the current value. As a result, at the end of each iteration the image data is reorganized. The structure and algorithm of the proposed neural network are presented along with various experimental results showing the capability of such a network to restore and enhance the gray-level images.

Original languageEnglish (US)
Pages503-510
Number of pages8
StatePublished - Dec 1 1990
Externally publishedYes
Event1990 International Joint Conference on Neural Networks - IJCNN 90 - San Diego, CA, USA
Duration: Jun 17 1990Jun 21 1990

Other

Other1990 International Joint Conference on Neural Networks - IJCNN 90
CitySan Diego, CA, USA
Period6/17/906/21/90

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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