基于可变步长PCNN的图像高斯噪声滤除

A new approach for noise reducing of image using variable step based on PCNN

  • 摘要: 针对高斯噪声的特点,在PCNN的基础上对灰度补偿模式作出改进,提出可变步长的灰度补偿模式的去噪方法.实验仿真表明,该方法对被高斯噪声污染的图像有较好的滤波效果,与相关的文献相比,在信噪比改善因子上体现了更好的性能.

     

    Abstract: In view of Gauss noise characteristic,this paper proposed a denoising method carries on the gradation compensation with the variable step based on Pulse-Coupled Neural Networks ( PCNN).This method worked well,and could manifest the PCNN's nature capture characteristic better compared with other filtering methods.The experiment results showed that it had manifested a better performance in the signal-to-noise ratio improvement factor.

     

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