# An Advanced AFWMF Model for Identifying High Random-Valued Impulse Noise for Image Processing
## 4 Experiment Results and Discussions
### Stop Iteration Comparison  
It is important to understand the iterative condition of the related ROR algorithm under the picture (or image) and its noise density. In this experiment, this study can interestingly verify the new stop criteria to stop iteration under the original image and noise density unknown and avoid the picture being over-repaired (i.e., the detail texture is removed). Regarding the environment of iteration, the stop condition is set up under the same ROR iterative architecture and the median filter is used to filter out the noise. The iteration number of the original ROR is shown in Table 2, which is based on the original image and the noise density. Referring to Equations (4)-(6), (18) and (19), the number of iterations for the new conditions is provided in this study. Furthermore, the noise density is selected at the low, medium, and high noise levels (30%, 50%, and 70%, respectively) from Table 2 using the same five images. As a result, the average PSNR of the five images for the verification results is shown in Tables 10 and 11, referring to Table 2 and using Equations (5) and (6), respectively. Table 10 uses the number of iterations estimated from the original ROR, while the stop condition using the PSRNR formula is obtained under the principle of an unknown original image and no noise information in Table 11. In the tables, it is shown that the comparable results \begin{table}
\begin{tabular}{l c c c c c c} \hline
**Model\(\backslash\)Ratio** & **Lena** & **Gold Hill** & **Boat** & **Peppers** & **Plane** & **Count** & **Rank (for Ratio 30\%)** \\ \hline BDND & 9 & 9 & 9 & 9 & 9 & 45 & 9 \\ DWM & 2 & 2 & 2 & 1 & 1 & 8 & 1 \\ EAIF & 7 & 6 & 5 & 6 & 8 & 32 & 6 \\ FRDFM & 8 & 8 & 7 & 8 & 6 & 37 & 8 \\ SBF & 5 & 3 & 4 & 5 & 4 & 21 & 4 \\ SDCOD & 6 & 5 & 8 & 7 & 7 & 33 & 7 \\ ROR & 3 & 7 & 6 & 3 & 5 & 24 & 5 \\ ASMF & 4 & 4 & 3 & 2 & 3 & 16 & 3 \\ ERDND & 10 & 10 & 10 & 9 & 9 & 48 & 10 \\ AFWMF & 1 & 1 & 1 & 4 & 2 & 9 & 2 \\ \hline \end{tabular}
\end{table} 
Table 7: The ranking of the 10 filter models according to the three experts’ average evaluations at 30% noise density for the five experimental images. \begin{table}
\begin{tabular}{l c c c c c c c} \hline
**Model\(\backslash\)Ratio** & **Lena** & **Gold Hill** & **Boat** & **Peppers** & **Plane** & **Count** & **Rank (for Ratio 50\%)** \\ \hline BDND & 9 & 9 & 9 & 9 & 9 & 45 & 9 \\ DWM & 3 & 3 & 3 & 3 & 3 & 15 & 3 \\ EAIF & 8 & 6 & 7 & 8 & 8 & 37 & 7 \\ FRDFM & 7 & 8 & 8 & 7 & 7 & 37 & 7 \\ SBF & 6 & 7 & 5 & 6 & 6 & 30 & 6 \\ SDCOD & 5 & 1 & 6 & 5 & 5 & 22 & 5 \\ ROR & 2 & 4 & 2 & 2 & 2 & 12 & 2 \\ ASMF & 4 & 2 & 4 & 4 & 4 & 18 & 4 \\ EBNDND & 9 & 9 & 9 & 9 & 9 & 45 & 9 \\
**AFWMF** & 1 & 2 & 1 & 1 & 1 & 6 & **1** \\ \hline \end{tabular}
\end{table} 
Table 8: The ranking of the 10 filter models according to the three experts’ average evaluations at 50% noise density for the five experimental images. \begin{table}
\begin{tabular}{l c c c c c c c} \hline
**Model\(\backslash\)Ratio** & **Lena** & **Gold Hill** & **Boat** & **Peppers** & **Plane** & **Count** & **Rank (for Ratio 70\%)** \\ \hline BDND & 9 & 10 & 9 & 9 & 9 & 46 & 9 \\ DWM & 4 & 4 & 3 & 3 & 6 & 20 & 4 \\ EAIF & 8 & 8 & 8 & 8 & 40 & 8 \\ FRDFM & 6 & 5 & 5 & 6 & 4 & 26 & 6 \\ SBF & 7 & 7 & 6 & 7 & 7 & 34 & 7 \\ SDCOD & 3 & 2 & 6 & 4 & 3 & 18 & 3 \\ ROR & 2 & 3 & 1 & 1 & 2 & 9 & 2 \\ ASMF & 5 & 6 & 4 & 5 & 5 & 25 & 5 \\ EBNDND & 10 & 8 & 9 & 10 & 10 & 47 & 10 \\
**AFWMF** & 1 & 1 & 2 & 2 & 1 & 7 & **1** \\ \hline \end{tabular}
\end{table} 
Table 9: The ranking of the 10 filter models according to the three experts’ average evaluations at 70% noise density for the five experimental images. of the PSNR value for the proposed AFWMF model are very close to the original ROR method, which uses the known noise density conditions. Therefore, the experimental results show that the proposed AFWMF model can effectively support the stop criterion for the iteration and obtain a set of optimal PSNR values that is very close to the known information conditions of the original image.