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

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.