平成24年度 電気関係学会四国支部連合大会

28

1-28
Noise Removal and Edge Detection
by Three-Layer Cellular Neural Networks
with A New Layer Arrangement
Muhammad IZZAT bin Mohd Idrus Yoshihiro KATO
Yoko UWATE Yoshifumi NISHIO(Tokushima University)

1. Introduction

Coupling[ T emplates : ]Cellular Neural Networks (CNN) were introduced by
L. O. Chua and Lin Yang [1]. The idea of the CNN wasinspired from the architecture of the cellular automata andthe neural networks. Compare to the conventional neuralnetworks, the CNN has local connectivity property. TheCNN has been researched in various ways such as image
processing and pattern recognition.
The two-layer CNN showed better performance than single layer CNN in some image processing [2]. Recently, threelayer CNN has been proposed to get better performance incolor image processing [3].
In this study, we propose a new layer arrangement of
three-layer CNN. We investigate the output characteristics
between the single layer CNN, two-layer CNN and the proposed structure by apply to the binary and gray-scale images for edge detection and noise removal simultaneously.

C1 =[C3 =(a)−1 −1 −1−1 8−1−1 −1 −10 00 ]0 −2 0.
0 00(b)[ 0, C2 =00010000]
,
(4)

(c)

(d)

Figure 2: Simulation results of binary image. (a) Input image.
(b) Output of the single layer CNN. (c) Output of the two-layer
CNN. (d) Output of the proposed structure.

2. Proposed Structure
(a)

(b)

(c)

(d)

Figure 3: Simulation results of gray-scale image. (a) Input image.
(b) Output of the single layer CNN. (c) Output of the two-layerCNN. (d) Output of the proposed structure.

Figure 1: Block Diagram of the proposed structure.
Figure 1 shows the arrangement of the proposed structure. The output of the ﬁrst layer CNN (CNN1) is delivered
into the second layer CNN (CNN2) and third layer CNN(CNN3) by through coupling template (C1 ). In other way,
the outputs of the CNN2 and the CNN3 are delivered into
the CNN1 by through coupling templates (C2 ) and (C3 )respectively. This arrangement is the diﬀerent point between the proposed structure and the previous structure ofthree-layer CNN.

3. Simulation Results
In this section, we show simulation results by using
the proposed structure. In this simulation, we use threetypes of templates which are “Small object remover”,“Edge detection” and “Smoothing”. This idea is inspired from previous study which was two templates“Edge detection” and “Smoothing” have been used in thetwo-layer CNN [2]. In our designed template, we simply
add one more template which is “Small object remover”
to remove some small noises in the image. The templatesof the proposed method are designed as follows.
T emplate of CN N 1 :[ 1A1 =

11121111], B1 =

T emplate of CN N 2 :[ 0A2 =00010000], B2 =

T emplate of CN N 3 :[ 0A3 =

Powered by TCPDF (www.tcpdf.org)

10121010], B3 =

[ 000

[ 000

[ 000000000000000000000], I1 = 0.

(1)], I2 = −1.

(2), I3 = −0.5.

(3)]Figure 2(a) and Fig. 3(a) show noisy images of binaryand gray-scale respectively. We compare the results between the single layer CNN, the two-layer CNN and theproposed structure. For the binary image, the output of thesingle layer CNN does not show almost anything diﬀerence
between input image. In Fig. 2(c), the output of the twolayer CNN shows that edge of square can be detected. However, the noises cannot be removed completely. In Fig. 2(d),the output of proposed structure shows that noises can beremoved in binary image and also edge of square can bedetected. For the gray-scale image, the output of the single
layer CNN does not show almost anything diﬀerence between input image. In Fig. 3(c) and Fig. 3(d), the output of
the two-layer CNN and the proposed structure are almost
same in detection of edge of circle. However, in noise removal’s performance, the output of the proposed structure
shows slightly better than the output of the two-layer CNN.
From these results, we can say that the proposed structure is the most eﬀective structure for this study of imageprocessing.

4. Conclusions
In this study, we have proposed a new layer arrangementof three-layer CNN. In the proposed structure, we only use
one single layer CNN to connect with the other two single
layer CNNs. From the simulation results of the proposedstructure, we can say that the proposed structure have potential in image processing.
References[1] L. O. Chua and L. Yang, “Cellular Neural Networks: Theory,”
IEEE Trans. Circuits Syst., vol. 32, pp. 1257-1272, Oct. 1988.
[2] Z. Yang, Y. Nishio and A. Ushida, “Image Processing of TwoLayer CNNs:Applications and Their Stability,” IEICE Trans. Fundamentals, vol. E85-A, No. 9, pp. 2052-2060, Sept. 2012.
[3] T. Inoue and Y. Nishio, “Research of Three-Layer Cellular Neural Networks Processing Color Images,” IEICE Technical Report,NLP2008-25, pp. 33-36, Jul. 2008.