## 2 Adaptive PCNN Combined with GWO and BEMD
### The Proposed BEMD-GWO-PCNN Algorithm

Table 5: Comparison of the adaptive PCNN combined with BEMD method with other current denoising methods corresponding to different noise intensity values \(\delta\) in Pepper. ## 4 Conclusions

With the aim of image noise suppression, an adaptive PCNN method optimized using the GWO and BEMD has been presented in this paper. Practical application of the proposed hybrid denoising algorithm has been achieved and yielded a better effect than other existing image denoising methods. In the proposed de-noising method, the PCNN filter is used for image denoising, while the GWO and BEMD are used as auxiliary algorithms to improve PCNN's noise reduction effects. To solve the critical problem of the parameter optimization issue, the GWO has been applied to global optimization through screening and iteration processes to derive optimized parameters. As a conclusion, the proposed method can be targeted more effectively to eliminate each decomposed component when BEMD is used, and the decomposed components have been treated differently by the PCNN filtering with specific critical parameters. Analyses of the denoising performance in the face of high-intensity noise has indicated that the faster parameter optimization convergence speed and better effects make it superior to other parameter optimization algorithms. The experimental outcomes analysis in the Pepper and Lena images have provided evidence for the effectiveness of the proposed BEMD-GWO-PCNN algorithm in image noise suppression applications. A higher PSNR value has represented the more valid statics preservation, which corresponds to more noise removal. Furthermore, our adopted indicators STD, MI, and SSIM are, in a high-level meaning, a closer degree to the size and distribution of the original image pixel points. However, as the accuracy and effectiveness of our algorithms increase significantly, the running complexity will increase slightly. In the future, we hope that we can further improve the convergence speed and accuracy level of the algorithm in order to keep refined. In this paper, we only address salt and pepper noise suppression. Thus, it is necessary to promote the suppression of other noise. Additionally, how to realize real-time image noise reduction and reduce the operation running time as much as possible, are also topics to be studied carefully. [PERSON] and [PERSON] contributed equally to this paper. Conceptualization, M.C. ; Software, Y.W. ; Validation, C.W. ; Formal Analysis, X.L. ; Funding Acquisition, C.S. This work was supported in part by the National Natural Science Foundation of China (61603353, 51705477, 51575500), and the Shanxi Province Science Foundation for Youths (201601D021067), and Research Project Supported by Shanxi Scholarship Council of China (2015-082) and the Fund for Shanxi \"1331 Project\" Key Subjects Construction. We thank [PERSON], from Liwen Bianji, Edanz Editing China (www.liwenbianji.cn/ac), for editing the English text of a draft of this manuscript. The authors declare no conflict of interest. ## References

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