# Gray Image Denoising Based on Array Stochastic Resonance and Improved Whale Optimization Algorithm
## 5 Image Denoising Based on Array Stochastic Resonance and Improved Whale Optimization Algorithm
### Magnetic Resonance Imaging Image  
An MRI grayscale image with pixel size of \(256\times 256\) was used for testing. The noise is Gaussian white noise with a mean value of 0 and an intensity of 0.3. Figure 8 shows the MRI grayscale image, the MRI noise image, the denoising MRI image by mean method, median method, Wiener method and Gaussian method. The PSNR performance comparison results of the processed MRI images are shown in Table 5. As can be seen from Table 5, compared with the PSNR of noisy MRI images, the PSNR value of MRI images after mean filtering, median filtering, Wiener filtering and Gaussian filtering is small, the PSNR value improvement effect is not obvious, and the image enhancement effect is not good. The image enhancement effect based on the array model method is shown in Figure 9. It can be seen from Figure 9 that with the increase of array unit size, the noise black and white spots on the restored image of the fixed parameter array model method, the parameter adaptive array model method based on WOA and the parameter adaptive array \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Array Unit N = 1** & **Array Unit N = 2** & **Array Unit N = 4** & **Array Unit N = 8** \\ \hline \multirow{2}{*}{Fixed parameters} & PSNR = 12.2744 & PSNR = 13.9195 & PSNR = 16.9321 & PSNR = 22.9212 \\  & a = 0.5; b = 1.5; h = 0.12 & a = 0.5; b = 1.5; h = 0.12 & a = 0.5; b = 1.5; h = 0.12 & a = 0.5; b = 1.5; h = 0.12 \\ \hline \multirow{2}{*}{WOA optimization} & PSNR = 15.3704 & PSNR = 19.3184 & PSNR = 25.926 & PSNR = 41.6294 \\  & a = 0.5; b = 0.12; h = 0.12 & a = 0.58; b = 0.80; h = 0.06 & a = 0.51; b = 0.19; h = 0.11 & a = 0.5; b = 5.00; h = 0.03 \\ \hline \multirow{2}{*}{IWOA optimization} & PSNR = 15.5307 & PSNR = 19.4159 & PSNR = 26.9586 & PSNR = 41.6387 \\  & a = 0.5; b = 1.37; h = 0.04 & a = 0.61; b = 3.23; h = 0.03 & a = 0.5; b = 4.88; h = 0.03 & a = 0.51; b = 1.25; h = 0.05 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Baboon image PSNR under different array saturation model methods (dB). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Image Name** & **Size** & **Noisy Image** & **Mean** & **Median** & **Wiener** & **Gaussian** \\ \hline Baboon & 256\(\times\)256 & 8.6783 & 16.5892 & 12.9568 & 15.9909 & 16.6008 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Baboon image under four classical filtering algorithms for PSNR (dB). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Image Name** & **Size** & **Noisy Image** & **Mean** & **Median** & **Wiener** & **Gaussian** \\ \hline Brain & 256 \(\times\) 256 & 8.6885 & 15.6739 & 14.1595 & 14.9413 & 15.6747 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: MRI image under four classical filtering algorithms for PSNR (dB). model method based on IWOA proposed in this paper become less and less, and the restored image gradually becomes clearer. Among them, under different array unit sizes, the restored images obtained by the parameter adaptive array model method based on IWOA proposed in this paper are the clearest. When the array unit size is 8, the restored image obtained by the IWOA-based parameter adaptive array model method proposed in this paper is very similar to the original image, with only a few noise points. Figure 7: Comparison of denoising effect of Baboon image. The comparison results of PSNR of the above three methods are shown in Table 6. As can be seen from Table 6, when the number of arrays is 1, the PSNR value obtained by the three model methods for image restoration is relatively small, and the processing effect is not good. With the increase of the size of the array element, the PSNR of the restored image of the three model methods also increases, so that the image restoration effect is better. Moreover, the PSNR value of the restored image of the IWOA-based parameter adaptive array model method proposed in this paper is the largest, and the image enhancement effect is the best.