# A Dual-Branch Self-Boosting Network Based on Noise2 Noise for Unsupervised Image Denoising
## 4 Experiments
### Ablation Experiment  
As mentioned earlier, the N2N model exhibits remarkable capability in recovering a clean image from a noisy input, although its overall performance on the entire image still falls short of that achieved by supervised state-of-the-art counterparts. This discrepancy arises from the absence of constraints imposed on the N2N model. To investigate the effects of different loss items, we conducted several experiments aimed at elucidating the impact of the loss item employed by our unsupervised approach. The experiments were conducted on the Set12 database with a noise level of \(\sigma=25\). We assessed various combinations of loss terms, namely, the N2N loss term \(\mathcal{L}_{N2N}\), the content loss term \(\mathcal{L}_{C}\), and the noise loss term \(\mathcal{L}_{N}\). Table 1 presents the results of each loss function during training. As shown in Table 1, while incorporating the loss term \(\mathcal{L}_{N2N}\) is crucial for training our model, adding either \(\mathcal{L}_{C}\) or \(\mathcal{L}_{N}\) alone can enhance the denoising effect. Furthermore, the simultaneous inclusion of both \(\mathcal{L}_{C}\) and \(\mathcal{L}_{N}\) leads to a significant performance improvement and enhances the stability of the learning process. The loss item specifically tailored for one branch will, in turn, enhance the performance of the other branch. The above experiment shows the self-boosting characteristics of the dual-branch structure.  
In the original N2N framework, the clean image generator employed was UNet. However, it has been observed that UNet is susceptible to rotation and scale variations in the input image, leading to unstable performance at different angles or scales. To overcome this limitation and enhance robustness, additional data augmentation or preprocessing steps are typically necessary. In our method, we utilize DnCNN as the clean image generator while excluding the skip connection and batch normalization layers (BN). A comparison between our approach and N2N was conducted on the Set12 dataset, incorporating various levels of noise. The results, presented in Table 2, clearly demonstrate the superior performance of our clean image generator using DnCNN.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(\mathcal{L}_{N2N}\) & \(\mathcal{L}_{Content}\) & \(\mathcal{L}_{Noise}\) & **PSNR** \\ \hline \(\surd\) & & & 31.05 \\ \(\surd\) & \(\surd\) & & 31.83 \\ \(\surd\) & & \(\surd\) & 31.76 \\ \(\surd\) & \(\surd\) & \(\surd\) & 32.21 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Ablation study with different combinations of loss items on the Set12 dataset (\(\sigma=25\)).  
The proposed approach utilizes a dual-branch network to generate both the clean image (content) and noise simultaneously. In order to showcase the efficacy of our dual-branch network, we conducted a comparative analysis with methods that directly predict noise and directly predict clean images. The outcomes, as presented in Table 3, highlight the effectiveness of the dual-branch network. The highest PSNR values are highlighted in bold. Notably, the PSNR result of the dual-branch network method exhibits a significant improvement compared to the PSNR result achieved using a single-branch network. This observation underscores the capability of our dual-branch strategy to facilitate productive collaboration between the content and noise branches, thereby yielding superior denoising performance.