# A Dual-Branch Self-Boosting Network Based on Noise2 Noise for Unsupervised Image Denoising
###### Abstract
Nonetheless, the performance of these denoising models still leaves room for improvement. In 2021, [PERSON] et al. introduced the Neighbor2 Neighbor (Ne2 Ne) denoising model, which employs a random neighbor sub-sampling strategy to generate noisy image pairs for training [26]. Ne2 Ne further reduces the acquisition difficulty of training images in the unsupervised model. However, the sampling operation reduces image resolution and may degrade denoising effectiveness. Moreover, Ne2 Ne is unsuitable for addressing spatially correlated noise and extremely dark images. In summary, while unsupervised denoising models can be trained without clear images, they are generally less effective than supervised models. In this work, our main objective is to enhance the performance of unsupervised denoising models. To achieve this, we propose a novel approach called the dual-branch self-boosting network (DSBNet) based on the Noise2 Noise (N2N) framework. DSBNet incorporates both residual mapping and nonresidual mapping in a dual learning manner, enabling accurate estimation of the latent clean image, i.e., denoised image. Unlike N2N, which generates a single latent clean image, DSBNet employs two separate branches within a single network framework. One branch is responsible for generating the noise component prediction, while the other branch predicts the content (clean) component. These two branches have their own strengths and complement each other. During unsupervised training, we leveraged multiple loss terms to facilitate mutual enhancement between the branches, eliminating the need for reference images. The training process involved generating noise and content prediction results using the dual-branch network. Then, while keeping the network parameters fixed, we fed these results back into the network to obtain new noise and content prediction results. By providing preliminary separation of image content or noise as input to the network, we expected the processed output to yield more accurate estimates, thereby improving the denoising performance. To guide the network training, we constructed a powerful loss function based on the network outputs which updates the parameter values. The inter-branch loss plays a crucial role in promoting the performance of both branches during unsupervised learning. During the inference stage, we only utilized the content prediction branch for image denoising. We conducted extensive evaluations on four public datasets (Set12, BSD68, SIDD, and DND) to validate the effectiveness of DSBNet. Additionally, ablation studies were performed to analyze the key modules and loss function, demonstrating the superiority of our method. DSBNet achieved significant performance improvements compared to previous unsupervised methods like N2N, surpassing even strongly supervised approaches when applied to synthetic and real-world noisy images, showcasing its excellent generalization capability. The code for DSBNet can be accessed at the following GitHub repository: [[https://github.com/gengyuhang/DBNet](https://github.com/gengyuhang/DBNet)]([https://github.com/gengyuhang/DBNet](https://github.com/gengyuhang/DBNet)) (accessed on 16 April 2024). The main contributions of our work can be summarized as follows:  
1. Denoising, a foundational task in image processing, can be approached through two distinct perspectives: directly predicting the image content or predicting the noise component of the image. To enhance the effectiveness of denoising, we propose a novel dual-branch network architecture. This architecture enables simultaneous prediction of both the noise component and the clean image within a unified frame work. By incorporating two parallel and complementary generators, our methodology effectively suppresses noise and extracts intricate image details, resulting in the reconstruction of a clean image.