2017 IEEE Workshop on Nonlinear Signal Processing, Feb. 10, 2017

Cellular Neural Networks with Switching
Two Templates for Image Processing
Takahisa Ando, Yoko Uwate and Yoshifumi Nishio
Departmrnt of Electrical and Electronic Engineering, Tokushima University
2-1 Minami-Josanjima, Tokushima 770-8506, Japan
E-mail: {ando, uwate, nishio}@ee.tokushima-u.ac.jp

I. I NTRODUCTION
In 1998, the Cellular Neural Networks (CNN) is introducedby L. O. Chua and L.Yang [1]. In the image processing of
the Cellular Neural Networks (CNN), it is difficult to processcomplex parts of the input image; edge, background, etc.
Some reserach are reported that it is possible to processcomplex parts by switching two templates which have thedifferent feature. Therefore, we propose, in this study, a newCNN method of switching two templates; 3 × 3 and 5 × 5templates. We apply our proposed method to edge detection
and investigate its performance.
II. O UR PROPOSED METHOD
The feature of our proposed method is switching two templates by using the maximum and the minimum output values(vymax : cell’s maximum output value, vymin : cell’s minimumoutput value) surrounding the center cell. The concept of ourproposed method is shown in Fig. 1. The processing steps ofour proposed method are described as followed:

Fig. 1.

III. S IMULATION R ESULTS
We apply our proposed method to edge detection. Figure 2shows the simulation results and Fig. 2 (a) shows the input
image. In Fig. 2 (b), edge lines are not detected clearly using
the 3 × 3 template. On the other hand, in Fig. 2 (c), edge linesof the text parts can be detected using the 5 × 5 template.
However, some noise remains in black areas of the input
image. In Fig. 2 (d), our proposed method can detect edge lines
clearly and receive less noise effect. Edge detection using the3 × 3 template is insusceptible to noise effect. Therefore, 3 × 3
template applies to black areas of the input image. Moreover,5 × 5 template applies to the text parts. Hence, our proposed
method removed noise by switching two templates.

Proposed CNN

Step 1: First, decide the center cell. Then, find the vymax ,and vymin from the 3 × 3 neighborhood.
Step 2: Secondly, calculate the difference value between
vymax and vymin ; |vymax − vymin |.
Step 3: Finally, determine the template by the thresholdvalue a according to the following equations.
3 × 3 template: |vymax − vymin | ≤ a
5 × 5 template: |vymax − vymin | > a.

(a)

(b)

(c)

(d)

Fig. 2. Simulation results. (a) Input image. (b) Simulation result using the3 × 3 template. (c) Simulation result using the 5 × 5 template. (d) Simulation
result of our proposed method (a = 1.8).

IV. C ONCLUSION
In this study, we have proposed a new CNN method ofswitching two templates. The simulation results show that ourproposed method is effective to detect edge lines of complex
parts and to remove some noise. Therefore, our proposed
method is more effective than the CNN in edge detection.

(1)

R EFERENCES[1] L. O. Chua and L. Yang, “Cellular Neural Networks: Theory,” IEEE
Trans. Circuits Syst, vol. 35, pp. 1257-1272, Oct. 1988.

This process is applied to all cells in the input image.
Step 4: Step1 to step3 are repeated every 0.15 [τ ].

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