令和2年度電気・電子・情報関係学会四国支部連合大会 講演論文集 (愛媛大学)
2020 SHIKOKU-SECTION JOINT CONVENTION RECORD OF THE INSTITUTES OF ELECTRICAL AND RELATED ENGINEERS (EHIME UNIV.)

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Maintaining Status of Image by Cellular Neural Networks
with Switching Two Templates
Kodai Kitamura Yoko Uwate Yoshifumi Nishio(Tokushima University)

1. Introduction

template.

In the image processing of the Cellular Neural Networks (CNN), there are methods to process a grayscale
image into a binary image. These methods turn into abinary image with decreasing amounts of information
depending on the number of calculations. However,there is little method how to maintain the process in thegrayscale image. Therefore, we propose a new methodof maintaining the status of the image with switching
two templates. We apply the proposed method to theimage and investigate its performance.

2. Proposed Method
In this section, we explain the proposed method byusing CNN. In our proposed method, two templates
are switched according to the input and output values(yij : cell’s output value, uij : cell’s input value) of surrounding the center cell. The processing steps of theproposed method are described as follows.
Step 1: Fix certain value a. Decide the center celland the size of 3×3 and 5×5 neighborhoods. Then,find the yij and uij from 5×5 range of the input andoutput values.
Step 2: Calculate the standard deviation within
3×3 and 5×5 ranges of the output value of the center
cell, and within 3×3 range of the input value of thecenter cell. Each value is defined as S1 , S2 and S3 .
Step 3: The template is applied to the center cell.
Switching condition is given as follows:S2 ≤ S1(1)aS1 ≥ S3 .
When either inequality of Eq. (1) is satisfied, it is applied 1st template. In the other case, it is applied 2ndtemplate.
Step 4: Step 1 to step 3 are applied to all cells andrepeated every 0.005 [τ ]. This process is conducted
every certain number of calculation n.

3. Simulation Results
In this section, we show the simulation results of theproposed method and the conventional CNN. Figure 1
shows the input image and the simulation resultsby the “Edge detection” template and the proposedmethod. In this simulation, 1st template is “Statevalue” template and 2nd template is“Edge detection”(a)(b)(c)(d)

Figure 1: Simulation results. (a) Input image. (b) Simulation
result of the “Edge detection” template. (c) Simulation result of
the proposed method(a = 0.85, n = 400). (d) Simulation resultof the proposed method(a = 0.85, n = 800).

Figure 1(a) shows input image including a bridge,
a lake and a forest. Figure 1(b) shows binary edge
detected image of Fig. 1(a). Figure 1(c) shows thegrayscale image with the information in Fig. 1(a). Figure 1(d) shows the output image increasing the numberof calculations compared to Fig. 1(c). There is littledifference between Fig. 1(c) and Fig. 1(d). From thesimulation results, the proposed method is effective inincreasing the number of calculations.

4. Conclusions
In this study, we have proposed a method of maintaining the status of the image with switching two templates. The simulation results show that the output
image is maintained as a grayscale image with the number of calculations. Therefore, the proposed method iseffective in maintaining status of the image.
In the future work, we will confirm that the proposed
method is effective for other image processings.