平成20年度電気関係学会四国支部連合大会

408

17-39

Processing Color Imagesby Three-Layer Cellular Neural Networks
Takashi INOUE
Yoshifumi NISHIO(Tokushima University)
1. Introduction
Cellular Neural Networks (CNN) [1] were introduced byChua and Yang in 1988. The idea of the CNN was inspired
from the architecture of the cellular automata and the neural networks. Unlike the conventional neural networks, theCNN has local connectivity property. Since the structureof the CNN resembles the structure of animals’ retina.
The humans retina have an ability distinguishing colorsand consist of three types of cells responding to the threeprimary colors. Roska et al. have proposed a concept
using a three-layer CNN processing the three primary colors in [2]. They have conﬁrmed that the three-layer CNN
could produce half-toned images of color images. However,after their pioneering work, there have not been many researches on the CNN dealing with three primary colors of
the image eﬀectively. In this study, we propose a threelayer CNN considering three primary colors (RGB-CNN).
In our RGB-CNN, the connections between the three layers play an important role. Namely, the three layers do
not operate independently but all the outputs inﬂuence
to the other layers. In this research, we conﬁrm that theRGB-CNN can enhance the edges of color images.

2. RGB-CNN
The proposed RGB-CNN is the three-layer CNN considering the three primary colors. Figure 1 shows the structure of the RGB-CNN. In Fig 1, CR, CG and CB are
the connection templates introduced to couple the threelayers. Before the processing, a color image is divided
into three primary colors and they are converted to threegray-scale images corresponding to red value, green value
and blue value. These three images are inputted to theRed-Layer, Green-Layer and Blue-Layer in RGB-CNN.

−1
BR = BG = BB =  −1−10b000 00
CG =  000
b000 00
CB =  000
b000 0

−1−1 −1

IR = IG = IB = − 1.

0
CR =  00−18−1

{{{b=2b=0if vyRij ≥ 0otherwiseb=2b=0if vyGij ≥ 0otherwise

b=2b=0if vyBij ≥ 0otherwise(2)

In Eq. (2), A, B and I are the same as those in Eq. (1).
For the connection template CR, CG and CB, we consider
the nonlinear (threshold-type) function to make the eﬀectof each layer more signiﬁcant.

(a)

(b)

(c)

Figure 2: Edge detection of fruit image. (a) Color input image. (b) Output image using CNN. (c) Output
image using RGB-CNN.
Figure 2 shows the simulation results for Edge detection. From these results, we can see that the proposededge-detection RGB-CNN produces the output image inwhich all the edges are enhanced and also the shaded area
remains as like a half-toned image.

Figure 1: Structure of the RGB-CNN.

3. Simulation results
In this section, we show some eﬀective edge detection
results using RGB-CNN. The templates are as follows.
Edge Detection T emplate in CN N :010−100  , B =  −1−10



−1−1  , I = −1.
−1(1)Edge Detection T emplate in RGB − CN N :0A= 00−18−10AR = AG = AB =  0001000 04. Conclusions
In this study, we have proposed the three-layer CNN
considering the three primary colors. By computer simulations of edge detection tasks for a color image, we have
conﬁrmed that the RGB-CNN could enhance the edges ofcolor images more eﬀectively than the regular processing
by using the original CNN. We feel that the RGB-CNN isavailable to various image processings.
References[1] L.O. Chua and L. Yang, “Cellular Neural Networks:Theory,” IEEE Trans. Circuits Syst., vol. 32, pp. 12571272, Oct. 1988.
[2] T. Roska, A. Zarandy and L.O. Chua, “Color Image
Processing by CNN,” Proc. of ECCTD’93, pp. 57-62, Aug.
1993.