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Methods for image processing and pattern formation in Cellular Neural Networks: a tutorial

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2 Author(s)
K. R. Crounse ; Electron. Res. Lab., California Univ., Berkeley, CA, USA ; L. O. Chua

In this paper, we demonstrate that many image processing and pattern formation effects of the simple Cellular Neural Network (CNN) can be understood by means of a common approach. By examining the dynamics in the frequency domain, when all CNN cells are in the linear region, the mechanisms for IIR spatial filtering, pattern formation, morphogenesis, and synergetics can be shown to be present, even though each cell has only first-order dynamics. In addition, the method allows many of the standard CNN templates, such as the nonlinear “averaging”, “halftoning”, and “diffusion” templates to be explained in a new light. Through many examples, it is shown how generalizations of these templates can be used to design linear and nonlinear filters for image processing tasks such as low-pass filtering, time-varying spatial filtering, and fingerprint enhancement

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IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications  (Volume:42 ,  Issue: 10 )