# A Nested UNet Based on Multi-Scale Feature Extraction for Mixed Gaussian-Impulse Removal
## 5 Experiment and Analysis
### Results  
This section describes the extensive experiments performed to evaluate MSNUNet. For all three noise types, we compared MSNUNet with six competing algorithms, including two traditional methods (WESNR [10] and LSLR [11]) and four CNN-based methods (TL-CNN [14], VA-CNN [16], DeGAN [18], and SACNN [20]). After applying the competing methods, we calculated the PSNR and structural similarity index (SSIM) metrics of each method's processing results to measure the effectiveness of the diverse mixed noise removal algorithms and evaluate the quality of the denoising results. In Tables 1-3, for each \(\sigma\) of a given test set, the first line shows the PSNR, and the second line shows the SSIM. As shown in Table 1, for the mixed AWGN+SPIN case, the denoising performance of MSNUNet outperforms all competing methods, which demonstrates the superiority of MSNUNet. Figure 4 illustrates the visual appearance of \"Barbara\" from the Set12 dataset. Figure 4b shows an image of a parrot corrupted by AWGN+SPIN (\(\sigma\) = 25, \(p_{sp}\) = 30%), while Figure 4c-i shows the processing results of the six compared algorithms and the proposed MSNUNet. Compared to the other methods, MSNUNet preserves more of the fine texture of the eye region, resulting in a significantly improved visualization. Figure 4: Denoising results on the image “Barbara”. (**a**) Original image. (**b**) Image corrupted with AWGN+SPIN (\(\sigma\) = 25, \(p_{sp}\)=30%). (**c**) WESNR [10]. (**d**) LSLR [11]. (**e**) TL-CNN [14]. (**f**) VA-CNN [16]. (**g**) DeGAN [18]. (**h**) SACNN [20]. (**i**) MSNUNet. Similarly, for the mixed AWGN+RVIN case, MSNUNet achieves the best denoising performance (Table 2). With increasing RVIN proportions and decreasing AWGN proportions, the denoising performance of the competing algorithms, except for LSLR [11], steadily improves, with the most significant improvements beingobserved for MSNUNet. The denoising results for image 24077 of the BSD100 dataset are shown in Figure 5, where Figure 5b is corrupted by AWGN+RVIN (\(\sigma\) = 20, \(p_{r}\) = 10%). In Figure 5, the visual effect obtained by MSNUNet is more pleasing than all the other results. For the mixed AWGN+SPIN+RVIN case, Table 3 reveals that our proposed MSNUNet delivers superior performance compared to nearly all the competing models at various mixed noise levels. Although the PSNR metric of SACNN [20] exceeds that of MSNUNet at \(\sigma\) = 5, \(p_{r}\) = 5%, and \(p_{sp}\) = 50%, our model exhibits exceptional denoising capability as the levels of AWGN and RVIN increase, highlighting its remarkable robustness. The denoising results for image 119082 of the BSD100 dataset are shown in Figure 6. Figure 6b shows images corrupted by AWGN+SPIN+RVIN (\(\sigma\) = 10, \(p_{r}\) = 10%, \(p_{sp}\) = 40%). As shown by the denoised images in Figure 6, DeGAN [18] and SACNN [20] effectively eliminate mixed noise and reconstruct clear image structures. WESNR [10] and LSLR [11] cannot clearly reconstruct the image's content, while TL-CNN [14] and VA-CNN [16] blur the texture and structure of the images; however, MSNUNet reconstructs fine textures more effectively than all other competing algorithms. To evaluate the generality of MSNUNet, we tested the performance of the model on the color versions of the BSD100 [38] and Urban100 [40] datasets under six different mixed noise ratios. The results are shown in Table 4. The denoising results of the proposed model are presented in Figures 7 and 8. It can be observed that MSNUNet achieves impressive performance in denoising color images. This demonstrates the versatility and effectiveness of our approach in handling color image denoising tasks. Figure 5: Denoising results on image 24077 of the BSD100 dataset. (**a**) Original image. (**b**) Image corrupted with AWGN+RVIN (\(\sigma\) = 20, \(p_{r}\) = 10%). (**c**) WESNR [10]. (**d**) LSLR [11]. (**e**) TL-CNN [14]. (**f**) VA-CNN [16]. (**g**) DeGAN [18]. (**h**) SACNN [20]. (**i**) MSNUNet.