Acta Scientiarum Naturalium Universitatis Pekinensis

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A Class of New Approach for Image Restoration Based on CNN

WANG Haiming, YU Daoheng1, GUO Shide   

  1. Department of Electronics, The national laboratory of machine perception; The institute of remote sensing and GIS, Peking University, Beijing, 100871, E-mail: Wang-haiming@263.net
  • Received:2002-09-10 Online:2003-09-20 Published:2003-09-20

一类用神经网络实现图像去噪的新方法

汪海明1, 余道衡1, 郭仕德2   

  1. 1北京大学信息科学技术学院,北京,100871; 2北京大学遥感与地理信息系统研究所,北京,100871,Email: Wang_haiming@263.net

Abstract: A new class of approach for image restoration based on two-dimensional Cellular Automata (CA) is proposed. By researching two new kinds of two-dimensional CA, we find two new classes of CA rules to design CNN, which can implement image restoration. The main idea is to design two CNNs with two antithetic CA rules. By this way, Image restoration can be implemented, getting obviously better results than the traditional ones. The simulation results prove our idea reasonable. It is expected to have profound influence on the technology of two-dimensional CA.

Key words: cellular automata(CA), cellular neural network(CNN), image restoration, joint processing

摘要: 提出了一类基于二维细胞自动机(CA)和细胞神经网络(CNN)的图像滤波新方法。通过对两种新的二维CA规则的深入研究,得出了两类CA演化规则能够用于设计新的细胞神经网络,可以实现灰度图像的噪声消除。核心思想是用两个对偶CA规则设计两个CNN网络,联合实现图像滤波处理,能够获得比传统算法好得多的处理速度和效果。仿真结果证明本文的想法是合理的,期望能够在二维CA研究与应用设计方面有所启发和突破。

关键词: 细胞自动机(CA), 细胞神经网络(CNN), 图像去噪, 联合处理

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