## 4 Results and Discussions
### Experimental Results
#### 4.3.2 Comparison with Other Different Models  
We performed a gray image contrast experiment using three different noise levels (\(\sigma\) = 15, 25, and 50). Image denoising experiments were carried out in two different datasets, Set12 and BSD68, and their respective PSNR and SSIM values were given. The test results are shown in Tables 4 and 5. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **Set12** & **BSD68** \\ \hline skip connection before dilated convolution & 30.519211 & 29.253672 \\ RDB without skip connection & 30.522240 & 29.249066 \\ DRFENet without DEB & 30.478824 & 29.232598 \\ SIRB without Multiplication of weights & 30.535542 & 29.254584 \\ DRFENet without FEB and SIRB without & 30.497805 & 29.244180 \\ Multiplication of weights & 30.266703 & 29.062368 \\ DRFENet & 30.545276 & 29.258824 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Average PSNR (dB) of ablation experiment on Set12 with noise levels of 25. We experimented on three different color image datasets (CBSD68, Kodak24, and McMaster) with six noise levels (\(\sigma\) = 15, 25, 35, 50, 75), and the corresponding PSNR is given in Table 6. The final experimental results show that the performance of our network model is better than that of other competitive algorithms. Many experimental results show that the current mainstream image denoising algorithm can restore the image's appearance by removing the noise. Still, this will make the image lose the sense of stereo. To better fit complex nonlinearities, the contour and background restoration of noisy images requires the model to have global perception ability and carry rich fine-grained information. Figure 3 is one of the images in the BSD68 dataset. We made a comparison diagram of the denoising effect to compare DRFENet with other denoising methods and enlarged part of the image for a clearer comparison. In Figure 4, we show a picture in CBSD68 to test the performance of DRFENet on color images. It can be seen that in Figure 4a-c, the network structure of DRFENet can not only restore the image more thoroughly, but also make the details of the picture smoother and retain a certain sense of stereo. As shown in Figure 4i, DRFENet can recover the contour of the image at a high noise level. These features are all due to DRFENet's network construction, which combines dilated convolution to the RDB for learning subtle features. To enhance the expression of noise in DRFENet, we create a DEB that establishes the connection between different layers at the initial stage of the model. Huge feature information from DEB is passed into the RDB through the long path and fused with the model mainline before dilated convolution. A large number of shallow features are captured by dilated convolution. It is equivalent to reusing the information learned by the shallow convolution layer in the model mainline. In addition, the features learned from the model fused the input noise map in FEB, and the feature map after feature sorting was used as the input of the attention mechanism in SIRB \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Methods** & \multicolumn{3}{c}{**BM3D**} & **TNRD** & **DnCNN** & **IRCNN** & **ECNDNet** & **ADNet** & **BRDNet** & **Ours** \\ \hline \multirow{2}{*}{\(\sigma=15\)} & PSNR & 31.08 & 31.42 & 31.72 & 31.57 & 31.72 & 31.73 & 31.72 & 31.76 \\ \cline{2-11}  & SSIM & 0.8722 & 0.8826 & 0.8905 & 0.8872 & 0.8906 & 0.8909 & 0.8909 & 0.8919 \\ \hline \multirow{2}{*}{\(\sigma=25\)} & PSNR & 28.57 & 28.92 & 29.20 & 29.07 & 29.21 & 29.23 & 29.24 & 29.26 \\ \cline{2-11}  & SSIM & 0.8017 & 0.8157 & 0.8269 & 0.8229 & 0.8274 & 0.8283 & 0.8293 & 0.8304 \\ \hline \multirow{2}{*}{\(\sigma=50\)} & PSNR & 25.62 & 25.97 & 26.22 & 26.09 & 26.22 & 26.26 & 26.28 & 26.29 \\ \cline{2-11}  & SSIM & 0.6869 & 0.7029 & 0.7183 & 0.7110 & 0.7190 & 0.7201 & 0.7230 & 0.7230 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Average PSNR (dB) of different methods on BSD68 with different noise levels of 15, 25, 50.