# 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
### Lena Image  
A Lena 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 4 shows the Lena gray image, the Lena noise image, and the denoising Lena image by mean method, median method, Wiener method and Gaussian method. It can be seen from the Figure 4 that in the strong noise environment, the restored image processed by the mean method, median method, Wiener method and Gaussian method has been seriously distorted, resulting in the blurring of the basic content of the image.  
The comparison of PSNR performance of mean filtering, median filtering, Wiener filtering and Gaussian filtering methods is shown in Table 1. Compared with the PSNR of noise Lena image, the PSNR of the four classical filtering methods is slightly increased, but as the increase is not more than two times, the enhancement effect is not obvious, and the image will become blurred after restoration. This means that the four classical filtering methods do not perform well in image restoration and cannot restore the image well.  
The denoising results of the array SR with fixed parameters, the parameter adaptive array SR based on WOA and our method on Lena image at the array number are N = 1, 2, 4, 8, as shown in Figure 5. It can be seen from Figure 5 that when the array unit is small, there are many noise points on the restored image, resulting in the poor visual effect of the image. With the increase of array units, the image restoration effect is enhanced, and the image is  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Image Name** & **Size** & **Noisy Image** & **Mean** & **Median** & **Wiener** & **Gaussian** \\ \hline Lena & 256 \(\times\) 256 & 8.7167 & 16.6138 & 13.3217 & 15.9101 & 16.6173 \\ \hline \end{tabular}
\end{table}
Table 1: Lena image under four classical filtering algorithms for PSNR (dB).  
Figure 4: Lena image enhancement for four classical filtering methods.  
clearer. Furthermore, among the restored Lena images processed by three model methods, the restored image processed by the IWOA-based parameter adaptive array model method proposed in this paper is the closest to the original image, which can better show the gray level and image content of the original image. Through comparing the denoising results of the above three methods, it can be clearly seen that our method proposed in this paper has the best denoising effect, which can better show the gray level and image content of the original image.  
Figure 5: Comparison of denoising effect of Lena image.  
The comparison results of PSNR of the above three methods are shown in Table 2. The empirical parameters a, b and h are selected for the array SR with fixed parameters, and the obtained PSNR is slightly larger than that of the traditional denoising method. The PSNR of the denoising result is greatly improved by whale optimization of the adaptive parameter SR. The proposed IWOA parametric adaptive array SR has the largest PSNR under different array numbers, and the enhancement of PSNR value is the largest when the array number is 8, increasing by 32.4266 dB. It can be seen that the parameter adaptive array model method proposed in this paper based on IWOA has better denoising ability.