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首页> 外文期刊>IEEE Transactions on Neural Networks >0.8 /spl mu/m CMOS implementation of weighted-order statistic image filter based on cellular neural network architecture
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0.8 /spl mu/m CMOS implementation of weighted-order statistic image filter based on cellular neural network architecture

机译：基于细胞神经网络架构的加权统计图像滤波器的0.8 / spl mu / m CMOS实现

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摘要

In this paper, a very large scale integration chip of an analog image weighted-order statistic (WOS) filter based on cellular neural network (CNN) architecture for real-time applications is described. The chip has been implemented in CMOS AMS 0.8 /spl mu/m technology. CNN-based filter consists of feedforward nonlinear template B operating within the window of 3 /spl times/ 3 pixels around the central pixel being filtered. The feedforward nonlinear CNN coefficients have been realized using programmable nonlinear coupler circuits. The WOS filter chip allows for processing of images with 300 pixels horizontal resolution. The resolution can be increased by cascading of the chips. Experimental results of basic circuit building blocks measurements are presented. Functional tests of the chip have been performed using a special test setup for PAL composite video signal processing. Using the setup real images have been filtered by WOS filter chip under test.
机译：本文介绍了一种基于细胞神经网络（CNN）架构的实时图像模拟大规模加权WOS滤波器集成芯片。该芯片已采用CMOS AMS 0.8 / spl mu / m技术实现。基于CNN的滤波器由前馈非线性模板B组成，该模板在被滤波的中心像素周围的3个像素/ spl次/ 3个像素的窗口内运行。前馈非线性CNN系数已使用可编程非线性耦合器电路实现。 WOS过滤器芯片可处理水平分辨率为300像素的图像。可以通过级联芯片来提高分辨率。给出了基本电路构件测量的实验结果。芯片的功能测试已使用用于PAL复合视频信号处理的特殊测试设置进行。使用该设置，真实图像已通过被测试的WOS过滤器芯片进行过滤。

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