# Gray Image Denoising Based on Array Stochastic Resonance and Improved Whale Optimization Algorithm
## 6 Conclusions  
Aiming at the poor enhancement effect of traditional denoising algorithms on strong noise images, this paper proposes an image denoising algorithm based on improved whale optimization algorithm and parameter adaptive array stochastic resonance, and applies it to the enhancement of noisy images. Firstly, the algorithm converts the gray image into an aperiodic one-dimensional binary pulse amplitude modulation (BPAM) signal by encoding binary symbols and modulating input signals through Hilbert dimensionality reduction  
Figure 9: Comparison of denoising effect of MRI image.  
scanning method. Then, the traditional whale optimization algorithm is improved in the initial solution distribution, global search ability and population diversity generalization, and the algorithm is applied to select the structural parameters of stochastic resonance processing module, which effectively improves the adaptive ability of array stochastic resonance model parameters. Finally, the output of the processing module is demodulated, decoded and reverse scanned to acquire the denoised image. Through the image processing simulation experiment of three different styles of images under a strong noise environment, the experiment is respectively compared with four classical filtering methods, the array model method with fixed parameters and the parameter adaptive array model method based on WOA. The experimental results show that, in terms of visual effects, the restored image processed by the image denoising algorithm 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. In terms of PSNR value, the PSNR value of the image denoising algorithm proposed in this paper is the largest in different array units. It can be seen that the image denoising algorithm proposed in this paper has a good performance in the restoration effect of the noised image. However, due to the long time consumption of array stochastic resonance, the algorithm proposed in this paper takes a long time in image denoising. At the same time, this paper mainly enhances the image in the strong white Gaussian noise background, but in the actual research, the types of noise are superimposed. In the future research, these two defects will be solved to further improve the performance of the algorithm applied in practice.  
Conceptualization, W.H. and S.J.; methodology, W.H. and S.J.; software, G.Z.; validation, W.H. and G.Z.; formal analysis, W.H.; investigation, S.J. and J.W.; resources, W.H. and G.Z.; data curation, J.W.; writing--original draft preparation, G.Z.; writing--review and editing, W.H.; visualization, W.H. and G.Z.; supervision, W.H., S.J. and J.W.; project administration, W.H. and S.J.; funding acquisition, S.J. All authors have read and agreed to the published version of the manuscript.  
This research is supported by the National Natural Science Foundation (NNSF) of China (NO.62073258, 62003261, 61871318), the Natural Basic Science Research Program of Shaanxi Province of China (NO. 2020)Q-650), and the Scientific Research Program funded by the Shaanxi Education Department (NO. 20)K0788).  
Informed Consent StatementInformed consent was obtained from all subjects involved in the study.  
Not applicable.  
The authors declare no conflict of interest.