平成23年度 電気関係学会四国支部連合大会

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Four-layer Cellular Neural Networks with Layerof Color and Luminosity

1. Introduction

Yoshihiro KATO Yasuhiro UEDA Yoko UWATE Yoshifumi NISHIO(Tokushima University){Cellular Neural Networks (CNN) were introduced by
L. O. Chua and L. Yang in 1988. The structure of CNN
resembles that of human’s retina. Human’s retina has thecapability to identify color and luminosity. The cell identiﬁes color is called a cone cell. On the other hand, the cell
reacting to luminosity is called a rod cell. The color image
processing using CNN was proposed. Additionally, Inoueet al. have used three-layer CNN based on cone cell which
processes the three primary colors of light (R, G, B), andperformed edge enhancement in [1] (RGB-CNN). However,
the edge of a low luminosity portion is not detected.
In this study, we propose the four-layer CNN considering
the three primary colors of light and the luminosity (CRCNN: Cone and Rod CNN).

2. CR-CNN
In this section, we explain the structure of the CR-CNN
as shown in Fig. 1. The CR-CNN is constructed by four
single-layer CNNs. Each layer corresponds to the three primary colors of light and luminosity, respectively. Each layer
is combined by connection template which are CR , CG , CB ,CL , CLR , CLG and CLB . Before the processing, a color image is converted to four gray-scale images. They correspondto red, green, blue and luminosity value, respectively. Thesefour gray-scale images are inputted to each layer in the proposed four-layer CNN.

3. Simulation Results
In this section, we show the simulation result for edge
detection by using the CR-CNN. In this simulation, size ofall templates are 3 × 3 matrix. In templates A and C, only
center value is the variable b, and other values of the template
are zero. Templates for edge detection by using the CR-CNN
are shown as follows.
Edge detection template in the CR-CNN :AR(2,2) = AG(2,2) = AB(2,2) = AL(2,2) = 1[BR = BG = BB = BL =IR = IG = IB = IL = −1{

CR(2,2) = b{
CG(2,2) = b{
CB(2,2) = b

Powered by TCPDF (www.tcpdf.org)−1−1−1−1
8−1−1−1−1

b = 0.9b=0

if vyRij ≥ 0otherwiseb = 0.9b=0

if vyGij ≥ 0otherwiseb = 0.9b=0

if vyBij ≥ 0otherwise]

if vyLij ≥ 0otherwise b = 0.9 if vyRij ≥ 0,CLR(2,2) = b




b=0

vyRij > vyGij andvyRij > vyBij
otherwiseb=0

vyGij > vyRij andvyGij > vyBij
otherwise

b=0

vyBij > vyRij andvyBij > vyGijotherwise b = 0.9 if vyGij ≥ 0,CLG(2,2) = b b = 0.9 if vyBij ≥ 0,CLB(2,2) = b(1)where A, B and I of each layer are the same as existing edge
detection template. Red, green and blue layers are inﬂuenced
through connection template (CB , CR , CG and CL ) fromblue, red and green layers, respectively and luminosity layer.
Luminosity layer is inﬂuenced though connection templates(CLR , CLG and CLB ) only from the cell whose value is thelarge among red, green and blue layer when the value is lager
than zero. In the case of each cell are the same output value,luminosity layer is inﬂuenced through connection template
only from one of them.

(a)Figure 1: Structure of the CR-CNN.

b=1
b=0

CL(2,2) = b

(b)

(c)

(d)

Figure 2: Simulation results. (a) Input image. (b) Simulation result using the basic CNN. (c) Simulation result
using the RGB-CNN. (d) Simulation result using theCR-CNN.
Figure 2 shows the simulation results of edge detection.
In Fig. 2 (a), this input image is a low luminosity image. InFig. 2 (b), using the basic CNN, most edge is not detected.
In Fig. 2 (c), using the RGB-CNN, this output image is
not edge detection image which would appear that the input
image is a low luminosity. In Fig. 2 (d), we can see that
the CR-CNN can detect the edges more eﬀectively than thebasic CNN and the RGB-CNN.
The simulation result using the CR-CNN arise from thestructure which consideration of three primary colors of lightand luminosity.

4. Conclusions
In this study, we have focused on retina cells which arecone and rod cell. Then, we have proposed four-layer cellularneural networks with layer of color and luminosity. Fromsimulation result, we have conﬁrmed that the CR-CNN could
detect the edge of color images more eﬀectively than thebasic CNN and RGB-CNN.
Reference[1] T. Inoue and Y. Nishio, “Edge Enhancement of Color Image byThree-Layer Cellular Neural Network Considering Three Primary Colors,” Proc. of NOLTA’08, pp. 540-543, Sep. 2008.