# An Advanced AFWMF Model for Identifying High Random-Valued Impulse Noise for Image Processing
## 5 Conclusions  
This study has proposed a new filtering framework called the AFWMF model. We proposed four key points and concerns relating to the image processing abilities of this new model. (1) First, this study transforms the original ROR algorithm to an expert system of fuzzy inference, since the use of fuzzy sets of fuzzy inference is a multi-value concept. Thus, the original ROR algorithm is combined with fuzzy inference in the proposed model; then, the empirical results show that the fuzzy ROR can be used more precisely to find noise from pictures (images). (2) Second, this study proposes a new stop condition of PSRNR. In the literature [12; 16; 25], the noise density and original image are used as references or standards for stop iteration; however, these studies exhibit some drawbacks (or problem), as they do not know what the original image or noise density is. Interestingly, the proposed PSRNR can bridge the gap of the above problem. (3) Third, in general, the median filter only uses a majority decision strategy. This strategy has a serious problem: the median filter is ineffective in filtering out noise when the noise density is over 50%. Thus, this study combines the use of directional medians and fuzzy inference to overcome this problem. (4) Finally, this study uses the fuzzy inference membership function, which can shift dynamically to accommodate random-valued impulse noise according to the distribution of the filter window; thus, it can improve the performance of image processing. The five well-known images, Lena, Peppers, Boat, Gold Hill, and Plane, were used as the experimental targets for evaluating the proposed AFWMF model. After all the experiments were run, this novel AFWMF model was found to significantly improve processing performances in the measurement of the PSNR value for preserving detail and fixed images at noise densities within the range of 20% to 70% for the experimental images. In extensive experiments, the method described in this study also showed a better performance in evaluating psycho-visual tests and identifying PSRNR than the other listed filter methods. Furthermore, the proposed AFWMF model also has better indicator of SSIM value. Conclusively, the proposed AFWMF model was generally found to be the best model among the listed 10 filtering methods for image processing in terms of the measurement of two quantitative indicators: PSNR and SSIM. From the experiments, five directions were found for presenting the empirical results of the proposed AFWMF model. (1) The proposed AFWMF model had a better performance in terms of the PSNR value and psycho-visual testing than the other filters for different random-valued impulse noises; however, the random-valued impulse noise is very difficult to remove from the related review of the scarce literature. (2) The proposed AFWMF model performed well in terms of restoration performance and sense of sight in this study. (3) The proposed AFWMF model showed a better restoration of detail or texture than the other filters, especially when the noise density ratio was within the range of 20 to 70%. (4) In total, this study provides good evidence that the proposed AFWMF model performs better in terms of PSNR and SSIE values. (5) Meaningfully, it was found that different impulse noise densities should be used with different filtering methods. It is important to find a suitable filtering model for processing images with various noise densities. Interestingly, three meaningful findings for subsequent image processing applications were identified: (1) The proposed AFWMF model was the best one for processing different impulse noise densities in terms of both the PSNR and SSIM values. (2) It is important to find an appropriate filtering method for use with a variety of images. (3) From the empirical results, it is evident that the DWM model can be used for a low 10% noise density, the ROR model can be used for high 80% and 90% noise densities, and the AFWMF model can be used with 20-70% noise densities.