# 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
### Baboon Image  
A Baboon 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 6 shows the Baboon grayscale image, the Baboon noise image, and the denoising Baboon image by mean method, median method, Wiener method and Gaussian method. As can be seen from the Figure 6, the traditional filtering algorithms make it difficult to remove the strong noise in the image. The PSNR performance after noisy Baboon image enhancement is shown in Table 3. As can be seen from Table 3, the PSNR of the restored image obtained by the four classical filtering methods is all small, the increase is not more than two times, the enhancement effect is not obvious, and the restored image will become fuzzy.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Array Unit N = 1** & **Array Unit N = 2** & **Array Unit N = 4** & **Array Unit N = 8** \\ \hline Fixed & PSNR = 12.0409 & PSNR = 13.7994 & PSNR = 17.0191 & PSNR = 22.9659 \\ parameters & 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 WOA & PSNR = 15.5079 & PSNR = 19.607 & PSNR = 26.7732 & PSNR = 40.5919 \\ optimization & a = 0.53; b = 0.64; h = 0.06 & a = 0.5; b = 3.14; h = 0.03 & a = 0.5; b = 0.86; h = 0.06 & a = 0.5; b = 1.05; h = 0.06 \\ \hline IWOA & PSNR = 15.5679 & PSNR = 26.4684 & PSNR = 26.9934 & PSNR = 41.1433 \\ optimization & a = 0.5; b = 1.21; h = 0.05 & a = 0.57; b = 0.48; h = 0.08 & a = 0.52; b = 1.17; h = 0.05 & a = 0.5; b = 0.67; h = 0.07 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Lena image PSNR under different array saturation model methods (dB).  
Figure 6: Baboon image enhancement for four classical filtering methods.  
Under different array element sizes, the restored images obtained by the array model method with fixed parameters (FP), the parameter adaptive array model method based on WOA and the parameter adaptive array model method based on IWOA proposed in this paper are shown in Figure 7. It can be seen from Figure 7 that with the increase of array unit size, the noise spots on the denoising image are reduced obviously by the three methods. Among them, under different array unit sizes, the denoising images obtained by our method are the clearest. When the array unit size is 8, the denoising image by our method is very similar to the original image, with only a few noise points.  
The comparison results of PSNR of the above three methods are shown in Table 4. In Table 4, when the number of arrays is 1, the PSNR of restored images obtained by the array model method with fixed parameters, the parameter adaptive array model method based on WOA and the parameter adaptive array model method based on IWOA proposed in this paper all increase, but the increase is small, and the image enhancement effect is still unclear. With the increase of the number of array elements, the PSNR of the restored image also increases, which makes the image restoration effect clearer. Moreover, in each array element, the PSNR of the restored image obtained by the IWOA-based parameter adaptive array model method is the maximum, which indicates that the method proposed in this paper has significant advantages in the process of gray image restoration.