# A Digital Denoising Method Based on Data Frequency Statistical Filtering
## 4 Conclusions  
This paper focuses on a filtering algorithm for random value impulse noise and proposes a filtering method based on probability statistics for medium- and low-density random value impulse noise. The main principle is to make use of the fact that the probability of noise points with low and medium density in the processing window is generally less than the probability of effective points. Setting the probability threshold, identifying and separating the noise points, and processing noise points separately while retaining effective points thus achieves the filtering effect. The noise reduction performance of the proposed method and other existing filtering methods, including median filtering, mean filtering, switching median filtering, and adaptive median filtering, are summarized and compared under medium- and low-density random value impulse noise. The main contributions can be summarized as follows:  
1. Through the experiment, it can be seen that the proposed method can effectively filter random impulse noise of medium and low density, and better preserve both image detail and quality. The noise reduction effect is superior to other filtering methods at each density, in terms of both subjective and objective evaluation.  
Figure 17: Performance comparison graph of several filtering algorithms.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Noise Density** & **Evaluation Criteria** & **Mean Filtering** & **Median Filtering** & **Switching Mean Filtering** & **Adaptive Mean Filtering** & **Filtering Method in This Paper** \\ \hline \multirow{2}{*}{20\%} & _PSNR_ & 30.7087 & 37.5883 & 36.1316 & 37.6569 & 38.8521 \\  & _SSIM_ & 0.6370 & 0.8788 & 0.3237 & 0.5049 & 0.8890 \\ \hline \multirow{2}{*}{30\%} & _PSNR_ & 29.7099 & 36.5909 & 33.9436 & 35.3701 & 36.5846 \\  & _SSIM_ & 0.5670 & 0.7935 & 0.1938 & 0.3201 & 0.8200 \\ \hline \multirow{2}{*}{40\%} & _PSNR_ & 29.0658 & 35.0756 & 32.4166 & 33.5336 & 35.0944 \\  & _SSIM_ & 0.5061 & 0.6523 & 0.1291 & 0.2123 & 0.7551 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental results for medium-density random value impulse noise.  
2. The structural similarity, \(SSIM\), of the new method proposed in this paper can be maintained above 0.9 under low-density noise conditions and above 0.75 under medium-density conditions, which is much better than other methods. The peak signal-to-noise ratio (\(PSNR\)) also demonstrates better stability than other methods at all densities, and the higher the noise density, the more obvious the advantage.
3. The method also has the following characteristics: an adjustable noise reduction effect, high operation efficiency, simple operation steps, shorter operation time, less resource occupation, etc. Therefore, it can realize efficient image processing and has certain practical value.  
Writing and analyzing, Z.L., T.L. ([PERSON]), and Y.L.; conceptualization and supervision, T.G. and T.L. ([PERSON]); methodology, T.L. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program (2020 YFB2009900), the Collaborative Innovation Platform of Fuzhou-Xiamen-Quanzhou Independent Innovation Demonstration Area (3502 ZCQXT202002), the Key Projects of the Natural Science Foundation of Fujian Province (2021J02013), the Natural Science Foundation of Fujian Province, China(2021J01295), and the Open Foundation of the State Key Laboratory of Fluid Power and Mechatoric Systems (GZKF-202026).  
Not applicable.