## 7 Conclusions  
Our research proposes a novel ultrasound image denoising algorithm on the basis of the improved Neighbor2 Neighbor unsupervised learning model. Our proposed method is compared with some other classical denoising techniques, such as BM3D, Noise2 Noise, Noise2 Void, etc. The experimental results show that the improved Neighbor2 Neighbor unsupervised learning model can effectively reduce the influence of speckle noise while maintaining the edge and detail information in the image, thus effectively improving the quality of the image. In addition, the proposed method also successfully addresses the issue that the original model cannot effectively suppress correlated or structured noise by adopting a new downsampling strategy, generating subsampled paired images as training images, and employing a self-supervised training mechanism with structural similarity loss. This improvement enhances the ability of the model in terms of detail retention. However, this research is trained and tested on a specific dataset, so the application of these results to other datasets may have certain limitations. For future work, we look forward to acquiring images from different devices and patient populations from collaborating clinical centers, thereby assessing the robustness and generalization of the algorithm in different environments. Moreover, we will also introduce evolutionary computation to optimize the hyperparameters of the network in the next work, which reduces the number of epochs, and achieves faster convergence of the model, thereby attaining our goal of improving the performance of the model. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Denoising Algorithms**} & \multicolumn{4}{c}{**Image1**} & \multicolumn{4}{c}{**Image2**} \\ \cline{2-9}  & **MSSIM** & **FSIM** & **EPI** & **Time (ms)** & **MSSIM** & **FSIM** & **EPI** & **Time (ms)** \\ \hline BM3D & 0.5520 & 0.7259 & 0.2518 & 620 & 0.5211 & 0.7060 & 0.2224 & 635 \\ Noise2 Noise & 0.4859 & 0.7217 & 0.3392 & 5.24 & 0.5133 & 0.6500 & 0.3418 & 5.33 \\ Noise2 Void & 0.5646 & 0.7434 & 0.3441 & 4.32 & 0.5364 & 0.7257 & 0.3334 & 4.56 \\ Neighbor2 Neighbor & 0.6086 & 0.8109 & 0.5494 & 6.78 & 0.5793 & 0.8056 & 0.5271 & 6.98 \\ Neighbor2 Neighbor + \(L_{\text{ssim}}\) & 0.6102 & 0.8244 & 0.5929 & 7.67 & 0.5951 & 0.8139 & 0.5609 & 7.84 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance comparison of different denoising methods (_MSSIM_, _FSIM_, _EPI_). **Author Contributions:** Conceptualization, P.W. and J.G. ; methodology, M.S. ; software, L.W. ; validation, J.G., L.W. and X.S. ; formal analysis, P.W. ; investigation, P.W. and L.W. ; resources, J.G. ; data curation, J.G. ; writing--original draft preparation, P.W. ; writing--review and editing, P.W. and L.W. ; visualization, P.W. and L.W. ; supervision, J.G., X.S. and M.S. ; project administration, M.S. ; funding acquisition, P.W. All authors have read and agreed to the published version of the manuscript. This research was funded by Sichuan University Students' Innovation and Entrepreneurship Training Program (S202210621090), Chengdu University of Information Technology Key Project of Education Reform (JY)G2022090/JYG2023212). Not applicable. The raw data supporting the conclusions of this article will be made available by the correspondence author on request. The authors declare no conflicts of interest. * [PERSON] (1988) [PERSON] Digital Image Processing for Noise Reduction in Medical Ultrasonics. Ph.D. Thesis, University of Edinburgh, Edinburgh, UK, 1988. * [PERSON] (1989) [PERSON]; [PERSON]; [PERSON] An adaptive weighted median filter for speckle suppression in medical ultrasonic images. _IEEE Trans. Circuits Syst._**1989**, _36_, 129-135. [CrossRef]
* [PERSON] et al. (2021) [PERSON]; [PERSON]; [PERSON] SSIM compliant modeling framework with denoising and deblurring applications. _IEEE Trans. Image Process._**2021**, _30_, 2611-2626. [CrossRef]
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