Nonlinear interpolative effect of feedback template for image processing by discrete-time cellular neural network

2003 ◽  
Author(s):  
N. Takahashi ◽  
T. Otake ◽  
M. Tanaka
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Discrete Time ◽  
Cellular Neural Network

NONLINEAR INTERPOLATIVE EFFECT OF FEEDBACK TEMPLATE FOR IMAGE PROCESSING BY DISCRETE-TIME CELLULAR NEURAL NETWORK

2003 ◽  
Vol 12 (04) ◽  
pp. 505-518 ◽  
Author(s):  
NOBUAKI TAKAHASHI ◽  
TSUYOSHI OTAKE ◽  
MAMORU TANAKA
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Discrete Time ◽  
Digital Signal Processor ◽  
Wavelet Transforms ◽  
High Speed ◽  
Digital Signal ◽  
Cellular Neural Network ◽  
Discrete Cosine Transformation ◽  
Processing Techniques

Recently a discrete-time cellular neural network (DT-CNN) is applied to many image processing applications such as compression and reconstruction, recognition and so on. Conventional image processing techniques such as the discrete cosine transformation (DCT) and wavelet transforms work as a simple filter and do not make good use of interpolative dynamics by the feedback A template, which is one of the significant characteristics of a cellular neural network (CNN). If CNN is applied to a filter by an only feedforward B template, one should make a model which consists of digital filters using high speed signal processing modules such as a high speed digital signal processor. This paper describes the nonlinear interpolative effect of the feedback A template, by showing the evaluation of image compression and reconstruction.

Basic Cellular Neural Networks Image Processing

2011 ◽  
pp. 218-222
Author(s):  
J. Álvaro Fernández
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Continuous Time ◽  
Engineering Application ◽  
Cellular Neural Network ◽  
Vlsi Circuits ◽  
Ann Model ◽  
Anatomy And Physiology ◽  
Artificial Neural Network Ann ◽  
Cellular Computer

Since its seminal publication in 1988, the Cellular Neural Network (CNN) (Chua & Yang, 1988) paradigm have attracted research community’s attention, mainly because of its ability for integrating complex computing processes into compact, real-time programmable analogic VLSI circuits (Rodríguez et al., 2004). Unlike cellular automata, the CNN model hosts nonlinear processors which, from analogic array inputs, in continuous time, generate analogic array outputs using a simple, repetitive scheme controlled by just a few real-valued parameters. CNN is the core of the revolutionary Analogic Cellular Computer, a programmable system whose structure is the so-called CNN Universal Machine (CNN-UM) (Roska & Chua, 1993). Analogic CNN computers mimic the anatomy and physiology of many sensory and processing organs with the additional capability of data and program storing (Chua & Roska, 2002). This article reviews the main features of this Artificial Neural Network (ANN) model and focuses on its outstanding and more exploited engineering application: Digital Image Processing (DIP).

MEMRISTOR CELLULAR AUTOMATA AND MEMRISTOR DISCRETE-TIME CELLULAR NEURAL NETWORKS

2009 ◽  
Vol 19 (11) ◽  
pp. 3605-3656 ◽  
Author(s):  
MAKOTO ITOH ◽  
LEON O. CHUA
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Cellular Automaton ◽  
Discrete Time ◽  
Cellular Neural Network ◽  
Brain Functions ◽  
Rsa Algorithm ◽  
Logical Operations ◽  
Almost All ◽  
Higher Brain Functions

In this paper, we design a cellular automaton and a discrete-time cellular neural network (DTCNN) using nonlinear passive memristors. They can perform a number of applications, such as logical operations, image processing operations, complex behaviors, higher brain functions, RSA algorithm, etc. By modifying the characteristics of nonlinear memristors, the memristor DTCNN can perform almost all functions of memristor cellular automaton. Furthermore, it can perform more than one function at the same time, that is, it allows multitasking.

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