## 4 Results and Discussions
### Experimental Results
#### 4.3.2 Comparison with Other Different Models
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Methods**} & \multicolumn{6}{c}{**PSNR**} & \multicolumn{6}{c}{**SSIM**} \\ \hline
**Images** & **C.man** & **House** & **Peppers** & **Starfish** & **Monarch** & **Airplane** & **Parrot** & **Lena** & **Barbara** & **Boat** & **Man** & **Couple** & **Average** & \\  & & & & & & Noise level \(\sigma=15\) & & & & & & & \\ BM3D & 31.65 & 34.76 & 32.56 & 30.89 & 31.86 & 31.72 & 31.31 & 34.22 & 32.87 & 32.10 & 31.94 & 32.05 & 32.28 & 0.8946 \\ DnCNN & 32.58 & 35.02 & 33.31 & 32.08 & 33.16 & 31.66 & 31.96 & 34.62 & 32.60 & 32.52 & 32.41 & 32.49 & 32.87 & 0.9030 \\ IRCNN & 32.35 & 34.70 & 33.19 & 31.94 & 32.85 & 31.61 & 31.82 & 34.41 & 32.55 & 32.33 & 32.29 & 32.  
through the Tanh activation function and a convolution layer. In other words, the product weighting feature with the previous mainline of the model has the quality of noise, which helps to strengthen the noise separation of the model. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Datesets** & **Methods** & \(\mathbf{\sigma=15}\) & \(\mathbf{\sigma=25}\) & \(\mathbf{\sigma=35}\) & \(\mathbf{\sigma=50}\) & \(\mathbf{\sigma=75}\) \\ \hline \multirow{6}{*}{CBSD68} & CBM3D & 33.52 & 30.71 & 28.89 & 27.38 & 25.74 \\ \cline{2-7}  & FFDNet & 33.80 & 31.18 & 29.57 & 27.96 & 26.24 \\ \cline{2-7}  & DrnCNN & 33.98 & 31.31 & 29.65 & 28.01 & – \\ \cline{2-7}  & IRCNN & 33.86 & 31.16 & 29.50 & 27.86 & – \\ \cline{2-7}  & ADNet & 33.99 & 31.31 & 29.66 & 28.04 & 26.34 \\ \cline{2-7}  & Ours & 34.04 & 31.34 & 29.68 & 28.11 & 26.40 \\ \hline \multirow{6}{*}{Kodak24} & CBM3D & 34.28 & 31.68 & 29.90 & 28.46 & 26.82 \\ \cline{2-7}  & FFDNet & 34.55 & 32.11 & 30.56 & 28.99 & 27.25 \\ \cline{2-7}  & DrnCNN & 34.73 & 32.23 & 30.64 & 29.02 & – \\ \cline{2-7}  & IRCNN & 34.56 & 32.03 & 30.43 & 28.81 & – \\ \cline{2-7}  & ADNet & 34.76 & 32.26 & 30.68 & 29.10 & 27.40 \\ \cline{2-7}  & Ours & 34.84 & 32.32 & 30.72 & 29.20 & 27.50 \\ \hline \multirow{6}{*}{McMaster} & CBM3D & 34.06 & 31.66 & 29.92 & 28.51 & 26.79 \\ \cline{2-7}  & FFDNet & 34.47 & 32.25 & 30.76 & 29.14 & 27.29 \\ \cline{2-7}  & DrnCNN & 34.80 & 32.47 & 30.91 & 29.21 & – \\ \cline{2-7}  & IRCNN & 34.58 & 32.18 & 30.59 & 28.91 & – \\ \cline{2-7}  & ADNet & 34.93 & 32.56 & 31.00 & 29.36 & 27.54 \\ \cline{2-7}  & Ours & 35.04 & 32.66 & 31.07 & 29.51 & 27.69 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: PSNR (dB) results of different methods on CBSD68, Kodak24, and McMaster datasets with noise levels of 15, 25, 35, 50, and 75. Figure 3: The image is derived from the results of different image-denoising methods of BSD68 at noise level 25. (**a**) Original image, (**b**) noisy image, (**c**) BM3D/PSNR = 32.88 dB/SSIM = 0.918, (**d**) DrnCNN/PSNR = 34.13 dB/SSIM = 0.934, (**e**) IRCNN/PSNR = 33.64 dB/SSIM = 0.928, (**f**) EC-NDNet/PSNR = 34.19 dB/SSIM = 0.936, (**g**) ADNet/PSNR = 34.21 dB/SSIM = 0.935, (**h**) BRDNet/PSNR = 34.22 dB/SSIM = 0.936, (**i**) DRFENet/PSNR = 34.29 dB/SSIM = 0.936. The experimental results show that DRFENet has strong robustness and leads in both PSNR and SSIM.