设计局部最大灰度值探测CNN模板的定理与应用

基于细胞神经网络(CellularNeuralNetwork,CNN)的图像处理的研究和应用已取得了很大进展。在图像处理中,有时需要确定图像各局部区域中最大灰度点的位置。文章对实现局部最大灰度值探测功能的CNN模板进行了理论分析和鲁棒性研究,提出了一个设计符合相应功能要求的鲁棒性CNN定理,它为设计相应的CNN模板参数提供了解析判据。通过数值模拟实例确认了理论结果在计算机图像处理中应用的有效性。     

Cellular Neural Networks for Gray Image Noise Cancellation Based on a Hybrid Linear Matrix Inequality and Particle Swarm Optimization Approach

This paper describes a technique for gray image noise cancellation. This method employs linear matrix inequality (LMI) and particle swarm optimization (PSO) based on cellular neural networks (CNN).We use two images that one is desired image and the other is corrupted to find the CNN template. The Lyapunov stability theorem is employed to derive the criterion for uniqueness and global asymptotic stability of the CNN equilibrium point. The current study characterizes the template design problem as a standard LMI problem and the optimization parameters of the templates are carried out by PSO. Finally, the examples are given to illustrate the effectiveness of the proposed method.     

基于CNN的分块自适应彩色图像边缘检测的研究*

利用细胞神经网络(CNN)模型对彩色图像边缘检测时,首先要解决彩色空间的选择以及颜色距离的计算问题,其次网络参数的选择也是一个重要问题。为了达到在确保边缘检测准确的同时有效抑制噪声的目的,对整幅图像进行分块自适应检测,采用熵来度量图像的各个子区域的不同性质,然后根据该区域的性质选择一组合适的网络参数,对提取该区域图像边缘的CNN 模板进行了理论分析和鲁棒性研究,提出一个设计符合相应功能要求的CNN 鲁棒性定理,它为设计相应的 CNN 模板参数提供了解析判据。仿真实验表明,该算法具有较好的健壮性。     

基于CNN模板的物体图像增长的研究与应用

基于细胞神经网络(CellularNeuralNetwork,CNN)的图像处理的研究和应用已取得了很大进展。在图像处理中,有时需要增大图像中的物体。本文对实现物体增长功能的CNN模板进行了理论分析和鲁棒性研究,提出一个设计符合相应功能要求的鲁棒性CNN定理,它为设计相应的CNN模板参数提供了解析判据。通过数值模拟实例确认了理论结果在计算机图像处理中应用的有效性。     

用细胞神经网络提取二值与灰度图象边缘

边缘是图象的重要特征,采用细胞神经网络提取图象边缘时,网络参数的选择是一个重要问题。为了能够有效地提取图象边缘,基于高通滤波模板,选择了细胞神经网络的一组简单易行的参数,首先将其用于检测二值图象边缘,再在此基础上,通过综合灰度值各位面边缘检测的结果提取出灰度图象的边缘。与传统边缘提取方法Sobel和Log方法的比较可见,该方法是有效的,并且由于细胞神经网络具有高速并行运算、便于硬件实现等特点,因此使其在图象实时处理中具有更大的潜力。     

Gradient Computation of Continuous-Time Cellular Neural/Nonlinear Networks with Linear Templates via the CNN Universal Machine

Single-layer, continuous-time cellular neural/nonlinear networks (CNN) are considered with linear templates. The networks are programmed by the template-parameters. A fundamental question in template training or adaptation is the gradient computation or approximation of the error as a function of the template parameters. Exact equations are developed for computing the gradients. These equations are similar to the CNN network equations, i.e. they have the same neighborhood and connectivity as the original CNN network. It is shown that a CNN network, with a modified output function, can compute the gradients. Thus, fast on-line gradient computation is possible via the CNN Universal Machine, which allows on-line adaptation and training. The method for computing the gradient on-chip is investigated and demonstrated.     

复合4邻域圈提取CNN的鲁棒性设计

细胞神经网络（Cellular Neural Network,CNN）于1988年由L.O.Chua 等人提出,已经成为一种处理图像和视频信号、机器人技术、生物视觉、高级大脑功能的新工具。细胞神经网络模板的鲁棒性设计是CNN在实际应用中碰到的重要课题之一。目的是设计一种能够提取复合4邻域圈的CNN,并对其模板进行鲁棒性设计,给出满足相应功能CNN的鲁棒性定理。该定理提供了能达到预先指定的图像处理功能的CNN模板参数不等式。数值模拟例子说明了理论证明的有效性。     

Color image processing in a cellular neural-network environment

When low-level hardware simulations of cellular neural networks (CNNs) are very costly for exploring new applications, the use of a behavioral simulator becomes indispensable. This paper presents a software prototype capable of performing image processing applications using CNNs. The software is based on a CNN multilayer structure in which each primary color is assigned to a unique layer. This allows an added flexibility as different processing applications can be performed in parallel. To be able to handle a full range of color tones, two novel color mapping schemes were derived. In the proposed schemes the color information is obtained from the cell's state rather than from its output. This modification is necessary because for many templates CNN has only binary stable outputs from which only either a fully saturated or a black color can be obtained. Additionally, a postprocessor capable of performing pixelwise logical operations among color layers was developed to enhance the results obtained from CNN. Examples in the areas of medical image processing, image restoration, and weather forecasting are provided to demonstrate the robustness of the software and the vast potential of CNN.     

Hybrid memristor/RTD structure-based cellular neural networks with applications in image processing

Cellular neural network (CNN) has been acted as a high-speed parallel analog signal processor gradually. However, recently, since the decrease in the size of transistor is going to approach the utmost, the transistor-based integrated circuit technology hits a bottleneck. As a result, the advantage of very large scale integration implementation of CNN becomes hard to really present, and further development of this era faces severe challenges unavoidably. In this study, two types of memristor-based cellular neural networks have been proposed. One type uses a memristor to replace the linear resistor in a conventional CNN cell circuit. And the other places a resonant tunneling diode (RTD) in this position and uses memristive synaptic connections to structure a hybrid memristor RTD CNN model. The excellent performances of the proposed CNNs are verified by conventional means of, for instance, stability analysis and efficient applications in image processing. Since both the memristor and the resonant tunneling diode are nanoscale, the size of the network circuits can be greatly reduced, and the integration density of the system will be significantly improved.     

基于细胞神经网络的视频分割算法研究

细胞神经网络（CNN）是一种局部互连的非线性并行模拟视觉处理系统,具有适合硬件实现处理速度快的优点,被广泛地应用于图像处理的各个方面。针对目前大多数视频分割算法难以满足实时性要求的缺点,将细胞神经网络应用到视频分割当中．提出了一种改进的基于细胞神经网络的视频分割算法,并通过仿真实验证明了其可行性。     

基于参数自适应CNN的灰度图像边缘检测

边缘是图像的重要特征。在应用细胞神经网络提取图像边缘时,网络的稳定性和参数的选择是关键。文中推导了细胞神经网络的稳定条件,并提出了网络参数的自适应设计思路。基于Matlab7.0平台,通过编写仿真程序,检测灰度图像边缘,得到良好效果。实验证明,该法还能有效抑制噪声的干扰。     

混沌神经网络模型及其应用研究综述

回顾了近年来混沌神经网络模型及其应用的研究进展．首先依据混沌产生的机理，将现有的多种类型混沌神经网络模型归结为4类典型的网络模型，并结合各种网络模型的数学描述来分析各自的机理和特性；然后从复杂问题优化、联想记忆和图像处理、网络与通信、模式识别、电力系统负荷建模和预测5个方面，介绍了混沌神经网络的应用现状；最后评述了混沌神经网络今后的研究方向和研究内容．     

Improving the accuracy of pruned network using knowledge distillation

Pattern Analysis and Applications - The introduction of convolutional neural networks (CNN) in image processing field has attracted researchers to explore the applications of CNN itself. Some...     

基于自适应遗传算法的细胞神经网络模板设计

细胞神经网络具有能够高速并行计算,易于硬件实现等特点,使其广泛应用于图像处理边缘提取、字符识别等诸多领域。细胞神经网络要正确实现不同的图像处理功能的关键在于模板参数的设计。提出一种基于自适应遗传算法求解模板参数的方法,一方面,通过对交叉概率和变异概率的改进以及遗传算子的设计,克服了基于简单遗传算法设计模板时算法容易早熟的不足;另一方面,采用准精确惩罚函数来设计适应度函数．降低了算法的运算量,提高了算法的收敛速度。给出了实例仿真结果,表明该方法的有效性。     

基于细胞神经网络的图像复原新方法

针对图像复原方法普遍运算量大的问题,提出了一种利用细胞神经网络进行图像复原的新方法,并首先提出了易于硬件实现的基于边缘方向判据的正则化复原方法;然后通过细胞神经网络的能量函数设计合适的网络参数来对该正则化函数进行细胞神经网络实现。仿真结果表明,该新方法是有效的,复原效果优于有约束的最小二乘复原法和已有的细胞神经网络图像复原法,而且由于细胞神经网络的并行性和硬件易实现性,使该新方法可以实时进行图像复原。     

Image processing applications via time-multiplexing cellular neural network simulator with numerical integration algorithms

A novel approach to simulate cellular neural networks (CNN) is presented in this paper. The approach, time-multiplexing simulation, is prompted by the need to simulate hardware models and test hardware implementations of CNN. For practical applications, due to hardware limitations, it is impossible to have a one-to-one mapping between the CNN hardware processors and all the pixels of the image. This simulator provides a solution by processing the input image block by block, with the number of pixels in a block being the same as the number of CNN processors in the hardware. The algorithm for implementing this simulator is presented along with popular numerical integration algorithms. Some simulation results and comparisons are also presented.     

细胞神经网在通信系统中的应用

细胞神经网(CNN)是一种大规模非线性模拟电路。它的两个重要特点是时间连续特性和局部连接特性,这使CNN在数字领域能实现实时、高速、并行的信号处理,并特别适于大规模集成电路(VLSI)的实现。本文阐述了CNN的结构和特点,并介绍了CNN在通信系统中的应用,主要包括信号处理及其硬件实现、混沌通信和通信中的优化问题等方面。     

基于细胞神经网络鲁棒性的彩色图像边缘检测

根据细胞神经网络(CNN)数学模型,提出一种新的彩色图像边缘检测方法。 新方法继承了CNN 的优点,解决了CNN 现有算法不能直接检测彩色图像边缘的问题。该 方法充分利用图像中的颜色信息,通过欧几里得距离度量像素之间的差异,使CNN 方程可 以在RGB 彩色空间中进行运算。对CNN 模板进行理论分析和鲁棒性研究,提出一个实现 彩色图像边缘检测功能要求的CNN 鲁棒性定理,为设计相应的CNN 模板参数提供了解析 判据。实验结果表明,该方法可以对彩色图像进行有效的边缘提取,定量评价验证了CNN 边缘检测定位准确的优点。     

Optimal solutions for cellular neural networks by paralleledhardware annealing

An engineering annealing method for optimal solutions of cellular neural networks is presented. Cellular neural networks are very promising in solving many scientific problems in image processing, pattern recognition, and optimization by the use of stored program with predetermined templates. Hardware annealing, which is a paralleled version of mean-field annealing in analog networks, is a highly efficient method of finding optimal solutions of cellular neural networks. It does not require any stochastic procedure and henceforth can be very fast. The generalized energy function of the network is first increased by reducing the voltage gain of each neuron. Then, the hardware annealing searches for the globally minimum energy state by continuously increasing the gain of neurons. The process of global optimization by the proposed annealing can be described by the eigenvalue problems in the time-varying dynamic system. In typical nonoptimization problems, it also provides enough stimulation to frozen neurons caused by ill-conditioned initial states.     

基于细胞神经网络的灰度图像负片算法

提出了一种基于细胞神经网络的灰度图像负片算法。根据细胞神经网络高速并行的特点。提出并设计了单层细胞神经网络负片模板用于灰度图像的负片处理。为细胞神经网络在图像处理领域中的应用提供了一种优良的算法。实验证明了，该算法对灰度图像负片处理的有效性。