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Proceedings Paper

Fuzzy cellular neural network for image enhancement
Author(s): Dandina Hulikunta Rao; P. I. Hosur
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Paper Abstract

Cellular neural networks (CNNs) are currently being used to arrive at solutions to the problems in image processing and pattern recognition. In this paper a technique for image enhancement using a fuzzy CNN is proposed. This technique exploits the massive parallelism of CNNs and mathematical framework of fuzzy logic to cope respectively with the computational complexity and uncertainty in noisy image. The mathematical model of discrete time cellular neural network (DTCNN) is obtained from the circuit equations of a cell. An architecture of fuzzy CNN for image enhancement is proposed. The network extracts the original image from a given noisy image by self organization. The fuzziness of the output image at each iteration is taken as a measure of error, which is in turn used to adapt the input image. An algorithm for adaptation of input image for linear and quadratic indices of fuzziness is derived. The efficacy of the proposed technique is verified through simulation results. Tests are carried out on noisy images obtained by adding zero mean Gaussian noise to synthetic bitonic images. The application of the proposed network for enhancement of noisy images with different noise levels is demonstrated.

Paper Details

Date Published: 4 March 1996
PDF: 11 pages
Proc. SPIE 2664, Applications of Artificial Neural Networks in Image Processing, (4 March 1996); doi: 10.1117/12.234264
Show Author Affiliations
Dandina Hulikunta Rao, Gogte Institute of Technology (India)
P. I. Hosur, Gogte Institute of Technology (India)

Published in SPIE Proceedings Vol. 2664:
Applications of Artificial Neural Networks in Image Processing
Nasser M. Nasrabadi; Aggelos K. Katsaggelos, Editor(s)

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