BEMD和狼群算法的自适应PCNN图像去噪方法

NOISE REMOVAL FOR IMAGE USING ADAPTIVE PULSE-COUPLED NEURAL NETWORK OPTIMIZED BY BIDIMENSIONAL EMPIRICAL MODE DECOMPOSITION AND GREY WOLF OPTIMIZATION

  • 摘要: 提出一种二维经验模态分解(Bidimensional Empirical Mode Decomposition,BEMD)和狼群算法(Grey Wolf Optimization,GWO)自适应脉冲耦合神经网络(Pulse Coupled Neural Network,PCNN)的复合图像去噪方法。通过BEMD将原始图像分解成多个二维固有模态函数分量和一个残余分量,用狼群算法对PCNN参数进行优化,对分解的各个分量进行去噪,并将去噪后的各分量进行重建得到去噪后的图像。主要优点包括:(1)有效确定PCNN关键参数,提高模型收敛速度;(2)有效解决高强度噪声的抑制问题;(3)通过对噪声点进行隔离并恢复原始像素点,最终使得图像细节信息得以完整保留。

     

    Abstract: A hybrid image denoising method based on an adaptive pulsed-couple neural network(PCNN)optimized by bidimensional empirical mode decomposition(BEMD)and the grey wolf optimization(GWO)is proposed. The BEMD decomposed an original image into various bidimensional intrinsic mode functions and a residual, and the decomposed components would be denoised by PCNN optimized with GWO, respectively. The wolf pack algorithm was used to optimize the PCNN parameters. A denoised image was obtained after reconstructing the denoised components. The advantages of this method include:(1)Deter-mining the key parameters of PCNN effectively and improving the convergence speed of the model;(2)Effectively solving the problem of high intensity noise suppression;(3)Preserving the details of the original image completely by isolating the noise points and recovering the original pixels.

     

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