# A Dual-Branch Self-Boosting Network Based on Noise2 Noise for Unsupervised Image Denoising
## 3 Methodology
### Dual-Branch Constraints  
Given the simplistic constraints utilized in training the N2N model, we propose a novel approach to leverage the dual-branch network to simultaneously generate image content and noise components. Subsequently, we reintroduce the generated image content and noise components back into the dual-branch network, thereby imposing stronger constraints on the denoising process. As shown in Figure 2, we utilize a dual-branch network denoted by \(f_{\theta}\) to decompose a given noisy image into its content and noise components, i.e., (\(I_{content}\), \(I_{noise}\)). Based on this, we denote the noisy image \(I_{noisy}\) as \(I_{noisy}=g(s_{1}I_{content},s_{2}I_{noise})\), where \(s_{1}=s_{2}=1\). Here, \(g\) serves as an image additive blending function. Following the decomposition of the provided noisy image into its constituent clean image and noise components, we can then input both the content and noise components back into the identical network, utilizing the exact same set of parameters \(\theta\) as in the initial image decomposition process. Ideally, since the predicted content image should not contain any noise, it can be modeled as \(I_{content}=g(s_{1}I_{content},s_{2}I_{noise})\), where \(s1=1\) and \(s2=0\). Similarly, the noise component should not contain any content; it can be modeled as \(I_{noise}=g(s_{1}I_{content},s_{2}I_{noise})\), where \(s1=0\) and \(s2=1\). Thus, these two conditions can be leveraged to formulate additional constraints for training the dual-branch network, thereby augmenting the efficacy of the N2N framework. The network learns to effectively separate the content and noise components and generate denoised images by leveraging the shared parameters \(\theta\). This approach capitalizes on the fact that the network can benefit from a nonresidual and residual learning process, where the same parameters are utilized for both the initial decomposition and the subsequent reintroduction of the components. Overall, dual-branch constraints promote a more robust and accurate denoising process, enabling the network to effectively exploit the relationships between the content and noise components, ultimately producing high-quality denoised images.  
Figure 2: The additional constraints established on the basis of the dual-branch architecture. Once the neural network \(f_{\theta}\) decomposes the input noisy image \(I_{noisy}\) into its content and noise components  
(\(I_{content}\),\(I_{noise}\)), these decomposed parts can be fed back into the network as inputs again. The resulting decomposed image components will be exclusive; when the image content is input into the network \(f_{\theta}\), it should generate content \(f_{content}(I_{content})\) on the content branch consistent with the input \(I_{content}\) while remaining completely unresponsive on the noise branch \(f_{noise}(I_{content})==\mathbf{0}\). Conversely, when the decomposed noise part \(I_{noise}\) is fed into the dual-branch network, the response obtained on the noise branch \(f_{noise}(I_{noise})\) should be identical to the input noise component \(I_{noise}\) while remaining completely unresponsive on the image content branch \(f_{content}(I_{noise})==\mathbf{0}\). Based on these constraints, network training will become more stable and capable of generalization.