International Journal of Computer Science & Network Security

International Journal of Computer Science & Network Security (국제컴퓨터통신보호논문지학회)
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New Cellular Neural Networks Template for Image Halftoning based on Bayesian Rough Sets

  • Elsayed Radwan (Faculty of Computer and Information Sciences, Mansoura University) ;
  • Basem Y. Alkazemi (College of Computer and Information Systems Umm Al-Qura University) ;
  • Ahmed I. Sharaf (Deanship of Scientific Research, Umm Al-Qura University)
  • Received : 2023.04.05
  • Published : 2023.04.30
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Abstract

Image halftoning is a technique for varying grayscale images into two-tone binary images. Unfortunately, the static representation of an image-half toning, wherever each pixel intensity is combined by its local neighbors only, causes missing subjective problem. Also, the existing noise causes an instability criterion. In this paper an image half-toning is represented as a dynamical system for recognizing the global representation. Also, noise is reduced based on a probabilistic model. Since image half-toning is considered as 2-D matrix with a full connected pass, this structure is recognized by the dynamical system of Cellular Neural Networks (CNNs) which is defined by its template. Bayesian Rough Sets is used in exploiting the ideal CNNs construction that synthesis its dynamic. Also, Bayesian rough sets contribute to enhance the quality of the halftone image by removing noise and discovering the effective parameters in the CNNs template. The novelty of this method lies in finding a probabilistic based technique to discover the term of CNNs template and define new learning rules for CNNs internal work. A numerical experiment is conducted on image half-toning corrupted by Gaussian noise.

Keywords

Acknowledgement

The Author would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work under the Grant code (20UQU0074DSR)

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