# A Dual-Branch Self-Boosting Network Based on Noise2 Noise for Unsupervised Image Denoising
###### Abstract  
While unsupervised denoising models have shown progress in recent years, their noise reduction capabilities still lag behind those of supervised denoising models. This limitation can be attributed to the lack of effective constraints during training, which only utilizes noisy images and hinders further performance improvements In this work, we propose a novel dual-branch self-boosting network called DBSNet, which offers a straightforward and effective approach to image denoising. By leveraging task-dependent features, we exploit the intrinsic relationships between the two branches to enhance the effectiveness of our proposed model. Initially, we extend the classic Noise2 Noise (N2N) architecture by adding a new branch for noise component prediction to the existing single-branch network designed for content prediction. This expansion creates a dual-branch structure, enabling us to simultaneously decompose a given noisy image into its content (clean) and noise components. This enhancement allows us to establish stronger constraint conditions and construct more powerful loss functions to guide the training process. Furthermore, we replace the UNet structure in the N2N network with the proven DnCNN (Denoising Convolutional Neural Network) sequential network architecture, which enhances the nonlinear mapping capabilities of the DBSNet. This modification enables our dual-branch network to effectively map a noisy image to its content (clean) and noise components simultaneously. To further improve the stability and effectiveness of training, and consequently enhance the denoising performance, we introduce a feedback mechanism where the network's outputs, i.e., content and noise components, are fed back into the dual-branch network. This results in an enhanced loss function that ensures our model possesses excellent decomposition ability and further boosts the denoising performance. Extensive experiments conducted on both synthetic and real-world images demonstrate that the proposed DBSNet outperforms the unsupervised N2N denoising model as well as mainstream supervised models trained with supervised methods. Moreover, the evaluation results on real-world noisy images highlight the desirable generalization ability of DBSNet for practical denoising applications. image denoising; unsupervised training; training constraints; dual-branch network; feedback mechanism; denoising performance +
Footnote †: journal:from a noisy observation \(y\in\mathcal{I}\) corrupted by random noise \(n\), where both \(x\) and \(y\) belong to the image space \(\mathcal{I}\). In general, the noisy image \(y\) can be modeled as  
\[y=x+n \tag{1}\]  
It is common to assume that the entries of the noise vector \(n\) are mutually independent. Many image denoising methods rely on this assumption [14; 15]. Moreover, these methods assume that the image exhibits some statistically meaningful structure that can be exploited to remove the noise [14; 16]. For example, there is an expectation that certain structures of the image are repeated elsewhere in the image. Over the past decades, a multitude of image denoising techniques has emerged, leading to significant advancements in the performance of state-of-the-art methods. Generally, these approaches can be categorized into two main groups: model-based methods and deep-learning-based denoisers. Model-based techniques, exemplified by seminal works like BM3D [14], NLM [17], NCSR [16], and WNNM [18], excel in addressing denoising challenges across varying noise levels through model optimization procedures. Nevertheless, these approaches often incur significant computational overhead due to their intricate algorithmic intricacies. Moreover, model-based methodologies typically rely on handcrafted priors such as nonlocal self-similarity [17] and sparsity [16], which may lack the robustness needed to effectively capture intricate image structures.