# An Advanced AFWMF Model for Identifying High Random-Valued Impulse Noise for Image Processing
## 4 Experiment Results and Discussions
### Psycho-Visual Performance Comparison  
In order to evaluate the psycho-visual performance in detail preservation, this experiment uses the real restoration image as the testing target to measure different filters for all the five images used. The noise is also set in ratios of 10-90% noise densities, but the 60% random-valued impulse noise here is selected randomly and contaminated in the first figure for the result presentation of the psycho-visual test in this study. Thus, the experiments are run for evaluating the restoration images at 60% noise density (note: due to the need to cut down the complexity of excessive figures and tables, this study only displays the empirical results of 60% noise densities as a representative figure presentation for the five images in this subsection). As a result, Figures 13-17 show the measurement results for the psycho-visual performance comparison in these images for assessing different filtering techniques. Obviously, the proposed AFWMF model is still preferable when compared to the other filtering models by personal observation; however, in order to identify the testing results of psycho-visual comparison, this study invited three experts who specialize in this image processing field to help with the visual evaluation. In particular, Table 6 shows the average ranking of the performance comparison for a noise of 60% made by the three experts. In Table 6, it is indicated that the proposed AFWMF model is still the best one and has a better performance in psycho-visual testing for a ratio of 60% impulse noise density. From Table 6 and these figures, it is clear that the top three models are AFWMF, ROR, and DWM in terms of rank ordering. It is shown that the proposed AFWMF model  
Figure 12: Performance of the PSNR average value for the five experimental images at different random values of pulse noise density. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Ratio\textbackslash{}Image** & **Lena** & **Gold Hill** & **Boat** & **Peppers** & **Plane** & **The Best** \\ \hline
10\% & AFWMF & DWM & EAIF & DWM & DWM & **DWM** \\
20\% & AFWMF & AFWMF & AFWMF & AFWMF & DWM & **AFWMF** \\
30\% & AFWMF & AFWMF & AFWMF & ROR & DWM & **AFWMF** \\
40\% & AFWMF & AFWMF & AFWMF & AFWMF & ROR & **AFWMF** \\
50\% & AFWMF & ROR & AFWMF & AFWMF & AFWMF & **AFWMF** \\
60\% & AFWMF & AFWMF & AFWMF & ROR & ROR & **AFWMF** \\
70\% & AFWMF & AFWMF & AFWMF & ROR & AFWMF & **AFWMF** \\
80\% & ROR & ROR & ROR & ROR & ROR & **ROR** \\
90\% & ROR & ROR & ROR & ROR & ROR & **ROR** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The best filter model at different noise densities in the PSNR average value for the five experimental images. Figure 16: Repair map with a 60% random value of impulse noise for the Gold Hill image. Figure 15: Repair map with a 60% random value of impulse noise for the Boat image. In addition, to further evaluate the key impact of the 10 filtering models used for noise interference processing, this study also further describes different levels of noise density for the image processing to improve the visual evaluation of the three experts. They again ranked the images restored by the 10 filters from 10% to 90% of interference for the five experimental images. Subsequently, the average ranking of the performance by the same three experts was calculated and is shown in Tables 7-9 for different levels of noise: 30% (low), 50% (median), and 70% (high), respectively. From Tables 7-9, it is also clear that the proposed AFWMF model performs the second best at a 30% noise density and is the best one in both the 50% and 70% noise densities. In general, the proposed AFWMF model still has the best performance in psycho-visual comparison for the five images compared to the other listed filtering models both generally and comprehensively. Productively and meaningfully, the proposed AFWMF model has a better performance than the other listed filtering models in terms of psycho-visual comparison, regardless of the level (low, medium, or high) of noise density involved.