# A Dual-Branch Self-Boosting Network Based on Noise2 Noise for Unsupervised Image Denoising
## 4 Experiments
### Overall Performance Comparison  
To thoroughly evaluate the denoising effectiveness of DBSNet, we conducted comparative experiments on the grayscale image datasets Set12 and BSD68. We introduced Gaussian noise to the images at noise levels of 15, 25, and 50. In order to analyze the denoising performance of each method in more detail, we present the specific PSNR results for each image at noise levels 15, 25, and 50 in Tables 4-6. Additionally, Table 7 showcases the average PSNR results obtained from the BSD68 dataset. The highest PSNR values are highlighted in bold. By examining Tables 4-7, it becomes evident that our proposed unsupervised method, DBSNet, outperforms other unsupervised methods and surpasses the majority of state-of-the-art supervised methods in terms of PSNR. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Methods** & **BM3D** & **DnCNN** & **FFDNet** & **Restormer** & **IRCNN** & **SwinIR** & **FEUNet** & **N2N** & **Ours** \\ \hline Cameraman & 31.90 & 32.14 & 32.42 & 33.02 & 32.53 & 33.04 & 32.48 & 33.72 & **34.39** \\ House & 34.92 & 34.96 & 35.01 & **36.18** & 34.88 & 35.27 & 36.18 & 35.85 & 36.15 \\ Peppers & 32.71 & 33.09 & 33.10 & 33.63 & 33.21 & 33.66 & 33.35 & 34.45 & **34.67** \\ Starfish & 31.12 & 31.92 & 32.02 & 32.58 & 31.96 & 32.57 & 32.05 & 33.69 & **34.35** \\ Monarch & 31.95 & 33.08 & 32.77 & 33.73 & 32.98 & 33.68 & 32.88 & 34.58 & **35.17** \\ Airplane & 31.10 & 31.54 & 31.58 & 32.07 & 31.66 & 32.08 & 31.69 & 33.57 & **34.10** \\ Eagle & 31.31 & 31.64 & 31.77 & 32.14 & 31.88 & 32.16 & 31.96 & 33.20 & **33.49** \\ Lena & 34.23 & 34.52 & 34.63 & 34.99 & 34.50 & 34.97 & 34.73 & 35.10 & **36.06** \\ Barbara & 33.04 & 32.03 & 32.50 & 33.56 & 32.41 & 33.98 & 32.53 & 32.19 & 33.91 \\ Boat & 32.09 & 31.93 & 32.35 & 32.83 & 32.36 & 32.81 & 32.41 & 33.26 & **34.14** \\ Man & 31.90 & 31.98 & 32.40 & 32.63 & 32.36 & 32.67 & 32.43 & 33.33 & **34.68** \\ Couple & 32.04 & 32.38 & 32.45 & 32.81 & 32.37 & 32.83 & 32.54 & 33.47 & **34.28** \\ Avg. & 32.36 & 32.67 & 32.75 & 33.35 & 32.76 & 33.36 & 33.86 & 33.86 & **34.61** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: PSNR results of various competing methods on individual images from Set12 dataset corrupted with Gaussian noise (\(\sigma=15\)). \begin{table}
\begin{tabular}{c c c} \hline \hline  & \(\sigma=15\) & \(\sigma=25\) & \(\sigma=50\) \\ \hline Dual branch & **34.65** & **32.21** & **28.91** \\ Directly predict content & 33.67 & 30.29 & 25.71 \\ Directly predict noise & 33.08 & 30.69 & 27.53 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: PSNR(dB) results of networks using different branches on the Set12 dataset with noise level \(\sigma\) at 15, 25, and 50. To evaluate the performance of our proposed method in real-world denoising scenarios, we conducted experiments on the SIDD and DND datasets. We compared our denoising results with the results obtained with BDNet [39], DnCNN [15], R2R [40], RIDNet [41], N2V [24], AP-BSN [1], and MM-BSN [4] methods. The real-world image denoising performance of different methods on the SIDD and DND datasets is presented in Tables 8 and 9. The highest PSNR values are highlighted in bold. It is evident from the results that our proposed method exhibits significant superiority over the unsupervised methods R2R and N2V in terms of PSNR and the SSIM, surpassing the performance of the majority of supervised methods.