# An Improved Image Filtering Algorithm for Mixed Noise
## 3 Experiment
### Experimental Results and Analysis
At the same time, the FSIM of our algorithm is obviously better when the noise increases, which fully shows that the algorithm has stronger denoising ability and detail restoration ability for images with serious noise interference, and the data totally demonstrates that the algorithm is very robust. Relatively speaking, NCSR, TWSC, and WNNM also show good performance. Their metric values are relatively close, but the denoising effect of the NCSR algorithm is generally better. As described above, the KSVD algorithm has a relatively good denoising ability at low noise level, but its denoising effect decreases rapidly with the increase of noise level. Among all the algorithms, the denoising effect of OCTOBOS, relatively speaking, is the worst of the four metrics. In order to feel the denoising effect of each algorithm on the noise images intuitively, the denoised images of lena, boat, and peppers, when \(\rho/\sigma\) is \(0.09/0.03\), were randomly selected for comparison and analysis, and they are shown in Figures 7-9. At the same time, Figure 10 shows the line charts of the lena image on PSNR, RMSE, SSIM, and FSIM with different algorithms to help us make further comparison and analysis. Figure 7: Denoising effect of different algorithms on lena (\(\rho/\sigma=0.09/0.03\)). Figure 8: Denoising effect of different algorithms on boat (\(\rho/\sigma=0.09/0.03\)). Figure 9: _Cont._  
From these denoised images, we can see that, for the mixed noise with salt and pepper + Gaussian noise, several other algorithms, of which KSVD and OCTOBOS are the most prominent, cannot remove the noise cleanly, except for BM3D and our algorithm, and a certain number of spots remain on the image. It also shows that these algorithms have extremely limited ability to remove mixed noise and cannot maintain the effective restoration of image detail information. Moreover, they cannot well retain image edge information. The overall picture of the denoised image is slightly rough. The BM3D algorithm can effectively remove the mixed noise, but it is still insufficient to restore the details of the image. It damages the edge of the image to a certain extent, making the edge part look fuzzy and thus losing more details, which reflects the limited ability of the BM3D algorithm to remove the mixed noise. Relatively speaking, the denoised image of our algorithm is clean. On the premise of ensuring the overall quality of the image, it can successfully remove the mixed noise and can retain the details and edge information of the image more completely. Compared with other algorithms, it has the strongest image restoration ability. Furthermore, we can find that, from Figure 10, PSNR, SSIM, and FSIM are steadily decreasing and RMSE is steadily increasing with the rise in noise level. The four trend lines are displayed approximately as a straight line with a low slope, which fully illustrates the robustness and stability of our algorithm. However, we still need to point out that, compared with other algorithms, our algorithm has better denoising ability, but when the noise level increases, the denoised images will inevitably show over-smoothness and a reduced ability to restore details, which is what we need to improve on in our future work. Figure 9: Denoising effect of different algorithms on peppers (\(\rho/\sigma=0.09/0.03\)).