Maintaining Images by Cellular Neural Networks
with Switching Two Templates
Kodai Kitamura, Yoko Uwate and Yoshifumi Nishio
Dept. of Electrical and Electronic Engineering
Tokushima University
2-1 Minami-Josanjima, Tokushima 7708506, Japan
E-mail: {kitamura, uwate, nishio}@ee.tokushima-u.ac.jp
to the image and investigate its performance.

Abstract— The Cellular Neural Networks (CNN) was developedby Chua and Yang in 1998. The performance of the CNN
depends on the parameters which are called the template. TheCNN is applied to various image processing by changing thetemplate. The output image processed by CNN is a binary image.
Therefore, the unnecessary objects are removed in the process. In
this research, we propose a method of switching two templates tostop the image processing in a certain state and output in thegrayscale state.

II.

In this section, we describe the basic structure andprocessing flow of the simple CNN. The basic circuit unit ofthe CNN is called a cell. The cell consists of linear element andnonlinear element. The CNN is formed from an array of manycells. We show a two dimensional array composed of M × Ncells arranged in M rows and N columns. A cell is coupled with
only adjacent cells. Adjacent cells interact with one another.
Cells which do not couple with only adjacent cells have anindirect influence. The range which some cells influence one
cell is defined by neighborhood. We describe a state equationof the cell and an output equation of the cell below.

Keywords; Cellular Neural Networks; Image Processing

I.

INTRODUCTION

In recent years, it has advanced information society. Ingeneral, the von Neumann computer is used for many
information processing. However, the von Neumann computer
cannot process many information in real time. Therefore,
Neural Networks were proposed. The idea was based on thehuman’s nervous system. The Cellular Neural Networks(CNN) was introduced by L. O. Chua and L. Yang in 1988 [1].
Hence, the CNN has been successfully used for various
highspeed parallel signals processing applications such asimage processing, pattern recognition and so on [1]. The CNN
was paired Neural Networks with the Cellular Automata by L.
O. Chua in 1998 [2]. The CNN consists of cells connectedeach other and the structure of CNN resembles the structureof the animal’s retina [3]. Therefore, the CNN has been
applied to various image processing applications. The
performance of CNN depends on the parameters which arecalled the template. The template represents strength ofconnection between each cell. The template consists of thefeedback template, the feedforward template and the threshold.
In the image processing of the CNN, there are methods toprocess a grayscale image into a binary image. Some
researches are reported that it is possible to process complex
parts by switching two templates which have the different
feature[4]. These methods turn into a binary image withdecreasing amounts of information depending on the numberof calculations. However, there is little method how tomaintain the process in the grayscale image. Therefore, we
propose a new method of maintaining the status of the image
with switching two templates. We apply the proposed method

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State Equation:

dvx ij dtirir  vx ij     A i , j ;k ,l  v y  kl   t k i  r l  j  ririr   B i , j ;k ,l  vu  kl   t   T .

(1)

k i  r l  j  r

Output Equation:vyij   t  III.

12 v   t   1  v   t   1  . (2)
x ij

x ij

PROPOSED METHOD

In this section, we explain the proposed method by using
CNN. In our proposed method, two templates are switchedaccording 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 the proposed method are described asfollows.
Step 1: Fix certain value a. Decide the center cell and thesize of 3×3 and 5×5 neighborhoods. Then, find the yij and uij
from 5×5 range of the input and output values.

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Step 2: Calculate the standard deviation within 3×3 and5×5 ranges of the output value of the center cell, and within
3×3 range of the input value of the center cell. Each value isdefined as S1, S2 and S3.
Step 3: The template is applied to the center cell. Switchingcondition is given as follows: S 2  S1
aS1  S3

(3)(a)(b)

When either inequality of Eq. (3) is satisfied, it is applied 1sttemplate. In the other case, it is applied 2nd template.
Step 4: Step 1 to step 3 are applied to all cells and repeatedevery 0.005 [τ]. This process is conducted every certain
number of calculation n.
IV.

SIMULATION RESULTS(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 result of the proposed method(a = 0.85; n =800).

In this section, we show the simulation results of the
proposed method and the conventional CNN. Figure 1shows
the input image and the simulation results by the “Edgedetection” template and the proposed method. In thissimulation, 1st template is “State value” template and 2nd
template is “Edge detection” template. Using templates of theproposed method are described as follows [5].

Figure 1(a) shows input image including a bridge, a lake anda forest. Figure 1(b) shows binary edge detected image of Fig.
1(a). Figure 1(c) shows the grayscale image with theinformation in Fig. 1(a). Figure 1(d) shows the output image
increasing the number of calculations compared to Fig. 1(c).
There is little difference between Fig. 1(c) and Fig. 1(d). Fromthe simulation results, the proposed method is effective inincreasing the number of calculations.

State Value template:

0A = 0
00B = 0
0


0 0
1 0 ,0 0
0 0
0 0 , T = 0.

0 0V.

In this study, we have proposed a method of maintaining
the image with switching two templates. In the proposed
method, we focus on the standard deviation of the input image
and the output image. From the simulation results, the output
image is maintained as a grayscale image with the number ofcalculations. Therefore, the proposed method is effective inmaintaining the image. In the future work, we will confirm thatthe proposed method is effective for other image processings.

(4)

Edge Detection template:

0 0 0 A = 0 1 0  ,0 0 0 -1 -1 -1
B = -1 8 -1 , T = 0.
-1 -1 -1

REFERENCES
[1]

(5)

[2]
[3]
[4]

[5]

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CONCLUSION

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