# An Advanced AFWMF Model for Identifying High Random-Valued Impulse Noise for Image Processing
## 4 Experiment Results and Discussions
### Discussions for the Related Results  
From all the experimental results, the study raises some questions related to the different filtering models in the following two directions: (1) The AFWMF model is the best method for denoising signals in almost all noise levels to the Lena, Gold Hill, and Boat images; however, the performance in the Peppers and Plane images is slightly worse in terms of some ratios of noise density when compared to the DWM and ROR models. The possible reasons for the issues mentioned above are that the AFWMF model improves its filtering performance using a more complicated and varied feature, form, or style than that of the other listed filtering models mentioned in this study. (2) The relationship between PSNR and noise density in the 10 listed filter models and the advantages/disadvantages or characteristics of the 10 filter models are discussed in the following: (a) The BDND filter is proven to operate efficiently even under high noise densities. However, the critical parameter needs to be defined in the filtering step of the BDND algorithm. (b) The DWM is based on the differences between the current local pixel and its neighbors aligned with four main directions. Then, the minimum of these four direction indexes is used for impulse detection. It makes full use of the characteristics of impulse and edges to detect and restore noise. DWM has an excellent performance in low-noise conditions. (c) The EIAF algorithm consists of two steps: impulse noise fuzzy detection and impulse noise cancellation. The EAIF has a slightly better performance in PSNR evaluation with 10% and 20% noise. It ranks eighth out of ten models. (d) FRDFM generally has a better performance at a higher noise percentage. For example, under 80% and 90% noise, it ranks fifth among the 10 model  
Figure 18: Performance comparison of the SSIM average values for the five experimental images at different random values of pulse noise density. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Model\(|\)Ratio** & **10\%** & **20\%** & **30\%** & **40\%** & **50\%** & **60\%** & **70\%** & **80\%** & **90\%** &
\begin{tabular}{c} **Count** \\ **(Rank)** \\ \end{tabular} \\ \hline BDND & 0.60 (9) & 0.47 (9) & 0.28 (9) & 0.16 (9) & 0.16 (9) & 0.13 (9) & 0.10 (9) & 0.07 (9) & 0.03 (9) & 81 (9) \\ DWM & **0.96 (1)** & **0.94 (1)** & 0.88 (2) & 0.83 (3) & 0.71 (3) & 0.60 (3) & 0.40 (4) & 0.27 (5) & 0.21 (4) & 26 (3) \\ EAF & 0.81 (8) & 0.76 (8) & 0.67 (7) & 0.54 (7) & 0.41 (8) & 0.33 (7) & 0.20 (8) & 0.13 (8) & 0.09 (8) & 69 (8) \\ FRDFM & 0.85 (7) & 0.80 (7) & 0.64 (8) & 0.53 (8) & 0.43 (7) & 0.33 (7) & 0.24 (7) & 0.18 (7) & 0.11 (7) & 65 (7) \\ SBF & 0.93 (4) & 0.88 (4) & 0.82 (5) & 0.73 (6) & 0.61 (6) & 0.43 (6) & 0.30 (6) & 0.23 (6) & 0.16 (6) & 49 (6) \\ SDCOD & 0.90 (5) & 0.87 (5) & 0.85 (4) & 0.75 (4) & 0.66 (5) & 0.51 (5) & 0.57 (5) & 0.29 (4) & 0.18 (5) & 42 (5) \\ ROR & 0.88 (6) & 0.84 (6) & 0.79 (6) & 0.74 (5) & 0.69 (4) & 0.60 (3) & 0.52 (3) & 0.44 (3) & 0.24 (3) & 39 (4) \\ ASMF & 0.94 (3) & 0.91 (3) & 0.87 (3) & **0.84 (1)** & **0.80 (1)** & 0.75 (2) & 0.66 (2) & **0.51 (1)** & **0.35 (1)** & 17 (2) \\ EBNDND & 0.44 (10) & 0.22 (10) & 0.17 (10) & 0.13 (10) & 0.09 (10) & 0.08 (10) & 0.05 (10) & 0.03 (10) & 0.02 (10) & 90 (10) \\ AFWMF & 0.95 (2) & 0.92 (2) & **0.89 (1)** & **0.84 (1)** & **0.80 (1)** & **0.77 (1)** & **0.68 (1)** & 0.49 (2) & 0.33 (2) & **13 (1)** \\ \hline \hline \end{tabular}
\end{table} 
Table 17: The SSIM average value for the five experimental images at different noise densities from the 10 filter models. methods. However, it only ranks seventh in terms of its overall ranking. (e) SBF ranks sixth overall and has an average performance at various noise percentages. (f) SDOOD ranks fifth, but he ranks poorly in low noise and better in high noise conditions, ranking third with 90% noise.