## 4 Experimental Results and Discussion
### Results and Discussion
#### 4.2.2 Contrastive Denoising with Continuous Increase in Noise Density
The PDBTMF algorithm exhibits relatively stable SSIM at low-density noise; however, it drops rapidly after around 50% noise density. The PDBTMF algorithm exhibits a more stable SSIM at noise densities of less than 70%. At lower noise densities not exceeding 25%, the ADTMF algorithm performs relatively well. However, the proposed improved TSETMF algorithm can achieve a higher and more stable SSIM above 0.71 at arbitrary noise  
Figure 10: Change map of PSNR with continuously increasing noise density for different algorithms processing Cameraman. Figure 9: Denoising effects of the TSETMF algorithm on the Lena image with the addition of different salt and pepper noise densities: **(a)** 30%, **(b)** 50%, **(c)** 70%, and **(d)** 90%. densities. This finding also further demonstrates that the proposed algorithm can process the Cameraman image with good high-density-noise denoising effects and excellent stable image quality. By utilizing the three classical algorithms, the PSNR and SSIM parameters were utilized to evaluate the abilities of the proposed improved TSETMF algorithm when processing the third classical image, COVID. Table S5 represents PSNR comparison data from different algorithms processing COVID with increasing noise density. Figure 12 presents a change map of PSNR with continuously increasing noise density for different algorithms processing COVID. As can be seen in Figure 12, the PSNR values of the PDBTMF, MDBMF, and ADTMF algorithms when processing the COVID image decreased by approximately 20 dB. Correspondingly, the larger negative slopes show that the three algorithms cannot handle the high-density salt and pepper noise of the COVID image. However, the decrease in the PSNR values of the proposed improved TSETMF algorithm is relatively minor, from 26.22 dB to 24.78 dB, with a mere 5.49% decline ratio. In particular, as the high-density noise varied from 5% to 95%, the proposed improved TSETMF algorithm exhibited the most stable denoising properties. In other words, this TSETMF algorithm works independently of the effects of noise density and achieves the most accurate and effective processing of the high-density salt and pepper noise in the COVID image. Table S6 represents SSIM comparison data from different algorithms processing COVID with increasing noise density. Figure 13 presents a change map of the SSIM with continuously increasing noise density for different algorithms processing COVID. The SSIM values of the PDBTMF algorithm express a declining tendency with increasing noise density but are inferior at higher-density noise. At the same time, the SSIM values of the PDBTMF algorithm decline sharply when the noise density is larger than 50%. However, the proposed improved TSETMF algorithm achieves a more stable SSIM above 0.51 at arbitrary noise densities. It should be illustrated that the SSIM is located at a relatively lower level; however, this TSETMF algorithm exhibits the most stable processing effects in terms of the denoising of high-density salt and pepper noise in the COVID image. Figure 11: Change map of SSIM with continuously increasing noise density for different algorithms processing Cameraman. By comparing the processing effects of the three standard images of Lena, Cameraman, and COVID presented in Figures 6, 10 and 12, the PSNR values of the PDBTMF algorithm and the MDBMF algorithm can be used to show that they have relatively better denoising effects. This is due to the specially designed filter's ability to process salt and pepper noise; whereas, it is not applicable to Gaussian noise or other noise. By contrast, the proposed improved TSETMF algorithm and the ADTMF algorithm can be utilized to address different types of noise. As can be observed from Figures 7, 11 and 13, it is obvious that the SSIM values obtained by the proposed improved TSETMF algorithm were the highest for Lena, followed by Cameraman and COVID, respectively.