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基于差值控制細胞神經網絡圖像濾波器

Image filters based on difference-controlled cellular neural networks

  • 摘要: 差值控制細胞神經網能夠實現灰度圖像濾波等復雜運算.針對原有差值控制細胞神經網中值濾波器在穩定性和可實現性上存在的不足,提出了一種偽中值濾波器(CNN PM-filter),進而引入Mask圖構造了選點式偽中值濾波器.從實驗結果和相關度分析可以看出,本文提出的兩種濾波器在改善穩定性與實現性的同時,沒有影響到濾波器的性能,而選點式偽中值濾波器能有效降低濾波造成的模糊圖像,取得更佳處理效果.

     

    Abstract: Difference-controlled Cellular Neural Networks (CNN) can realize some complex operations more convenient than standard CNN. To improve the stability and realizability of existent median filters based on difference-controlled CNN, a pseudo median filter based on difference-controlled CNN (CNN PM-filter) was presented. In order to reduce image blur caused by filtering, a selective CNN PM-filter was studied too. The results of signal/noise ratio and correlation degree show that the stability and realizability of the two filters in paper were improved. The CNN PM-filter's performance is a little better than a standard median filter; the selective CNN PM-filter can suppress impulse noise and simultaneously reduce image blur effectively.

     

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