## 4 Experiments
### Visual Comparisons  
Visual quality is a crucial indicator for evaluating denoising effects in image processing. Therefore, a visual comparison experiment was conducted on multiple test images with rich texture information. We invited graduate students from different grades in the laboratory to evaluate visual images. We asked the students, who range in age from approximately 18 to 24 years old, to observe these images for about 5 minutes before making \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{6}{c}{**Noise Level**} \\ \cline{2-7}  & \(\sigma\) = 10 & \(\sigma\) = 20 & \(\sigma\) = 30 & \(\sigma\) = 40 & \(\sigma\) = 50 & \(\sigma\) = 60 \\ \hline BM3D & 0.9471 & 0.8971 & 0.8534 & 0.8076 & 0.7767 & 0.744 \\ FFDNet & 0.9461 & 0.9045 & 0.8700 & 0.8377 & 0.8074 & 0.7790 \\ NCSR & 0.9471 & 0.9471 & 0.8560 & 0.8149 & 0.7788 & 0.7442 \\ DrCNN & 0.9462 & 0.8891 & 0.8437 & 0.8100 & 0.7749 & 0.7417 \\ WNNM & 0.9486 & 0.9041 & 0.8690 & 0.8327 & 0.8050 & 0.7749 \\ RED-Net & 0.9588 & 0.9139 & 0.8818 & 0.8486 & 0.8119 & 0.7531 \\ TWSC & 0.9493 & 0.9069 & 0.8692 & 0.8330 & 0.7986 & 0.7659 \\ CSNet & 0.9547 & 0.9131 & 0.8826 & 0.8499 & 0.8150 & 0.7447 \\ DIP & 0.9474 & 0.9001 & 0.8624 & 0.8305 & 0.7996 & 0.7732 \\ Proposed & **0.9692** & **0.9579** & **0.9180** & **0.8909** & **0.8772** & **0.8433** \\ \hline \hline \end{tabular}
\end{table} 
Table 16: Average SSIM results of different methods on 10 images in Urban100 with various noise levels. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{6}{c}{**Noise Level**} \\ \cline{2-7}  & \(\sigma\) = 10 & \(\sigma\) = 20 & \(\sigma\) = 30 & \(\sigma\) = 40 & \(\sigma\) = 50 & \(\sigma\) = 60 \\ \hline BM3D & 0.9778 & 0.9571 & 0.9387 & 0.9191 & 0.9018 & 0.8853 \\ FFDNet & 0.9774 & 0.9577 & 0.9410 & 0.9256 & 0.9105 & 0.8959 \\ NCSR & 0.9782 & 0.9573 & 0.9385 & 0.9204 & 0.9002 & 0.8823 \\ DrCNN & 0.9781 & 0.9572 & 0.9387 & 0.9219 & 0.9059 & 0.8885 \\ WNNM & 0.9787 & 0.9592 & 0.9433 & 0.9269 & 0.9122 & 0.8967 \\ RED-Net & 0.9757 & 0.9523 & 0.9289 & 0.9070 & 0.8783 & 0.8586 \\ TWSC & 0.9789 & 0.9599 & 0.9429 & 0.9260 & 0.9086 & 0.8912 \\ CsNet & 0.9749 & 0.9510 & 0.9267 & 0.9047 & 0.8758 & 0.8542 \\ DIP & 0.9724 & 0.9497 & 0.9308 & 0.9134 & 0.8963 & 0.8791 \\ Proposed & **0.9801** & **0.9767** & **0.9555** & **0.9421** & **0.9368** & **0.9168** \\ \hline \hline \end{tabular}
\end{table} 
Table 17: Average FSIM results of different methods on 10 images in Urban100 with various noise levels. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{6}{c}{**Noise Level**} \\ \cline{2-7}  & \(\sigma\) = 10 & \(\sigma\) = 20 & \(\sigma\) = 30 & \(\sigma\) = 40 & \(\sigma\) = 50 & \(\sigma\) = 60 \\ \hline BM3D & 0.9778 & 0.9571 & 0.9387 & 0.9191 & 0.9018 & 0.8853 \\ FFDNet & 0.9774 & 0.9577 & 0.9410 & 0.9256 & 0.9105 & 0.8959 \\ NCSR & 0.9782 & 0.9573 & 0.9385 & 0.9204 & 0.9002 & 0.8823 \\ DrCNN & 0.9781 & 0.9572 & 0.9387 & 0.9219 & 0.9059 & 0.8885 \\ WNNM & 0.9787 & 0.9592 & 0.9433 & 0.9269 & 0.9122 & 0.8967 \\ RED-Net & 0.9757 & 0.9523 & 0.9289 & 0.9070 & 0.8783 & 0.8586 \\ TWSC & 0.9789 & 0.9599 & 0.9429 & 0.9260 & 0.9086 & 0.8912 \\ CsNet & 0.9749 & 0.9510 & 0.9267 & 0.9047 & 0.8758 & 0.8542 \\ DIP & 0.9724 & 0.9497 & 0.9308 & 0.9134 & 0.8963 & 0.8791 \\ Proposed & **0.9801** & **0.9767** & **0.9555** & **0.9421** & **0.9368** & **0.9168** \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Average PSNR results of different methods on 10 images in Urban100 with various noise levels. comments. Figure 11 shows a visual comparison of the denoising results of one image selected randomly from BSD with a noise level \(\sigma=40\). In the denoised images, we chose to evaluate a portion of the back thigh of a tiger, which was magnified and displayed in the bottom right corner of each image for better visualization. It can be found that DIP exhibited poor denoising performance as some details were lost.