# A Dual-Branch Self-Boosting Network Based on Noise2 Noise for Unsupervised Image Denoising
###### Abstract
Improved by their robust learning and representation capabilities, convolutional neural networks (CNNs) demonstrate superior performance in tasks related to image denoising. Over the past decade, methods based on deep neural networks (DNNs) have garnered remarkable success. One notable example is the DrCNN (Denoising Convolutional Neural Network) [15], which leverages residual learning and batch normalization to enhance both training efficiency and denoising efficacy. However, conventional CNN architectures may encounter challenges in handling objects across various locations and scales, particularly for larger targets or those with non-fixed positions within the image. To overcome the inherent limitations of CNN structures, the Transformer architecture has been introduced into low-level visual image processing. Proposed in 2022, Restormer [19] stands out as an efficient Transformer-based method capable of handling high-resolution images. It accomplishes this by modeling global context through self-attention mechanisms across channels, thereby reducing computational overhead. More recently, [PERSON] [20] proposed a pixel-wise crossmodal image denoising method (CMID) based on deep reinforcement learning, aiming to effectively address noise across different modalities. However, the performance of networks trained on synthetic data may significantly degrade when tested on real-world data due to domain gaps between synthetic and real datasets. Although existing supervised learning models demonstrate outstanding denoising performance, they often suffer from data bias issues, necessitating a substantial number of noisy-clean image pairs to enhance their generalization ability. However, acquiring such clear images poses a significant challenge. To mitigate this issue, researchers have proposed a series of unsupervised and self-supervised methods that obviate the need for clean images to train denoising networks. For instance, [PERSON] et al. [21] introduced a statistical point estimation procedure and devised the Noise2 Noise (N2N) denoising model, which advocates training models solely with corrupted image pairs for different noise distributions. This approach notably streamlines the training process by relaxing the demand for clean images. However, its performance may degrade when handling images divergent from the training dataset, and its generalization capability remains to be improved. Subsequently, [PERSON] et al. proposed an unsupervised method named Deep Image Prior (DIP) [22], which requires only a noisy image and a suitable network structure (e.g., UNet) to accomplish the denoising task, showcasing remarkable flexibility. DIP combines optimization iterations with the prior modeling prowess of deep neural networks (DNNs). As it solely relies on the noisy image, DIP exhibits desirable practical applicability. Nonetheless, the denoising performance of DIP and its variants still lags behind supervised or unsupervised learning, particularly for synthetic images. In contrast, Noise2 Void (N2V) [23] and Noise2 Self (N2S) [24] employ a blind spot strategy by randomly discarding some input pixels and utilizing their neighboring pixels to predict them. This approach helps prevent overfitting during DNN training for mapping noisy images to themselves. However, the large area occupied by blind spots on the inputs leads to valuable context loss in the receptive field of predicted pixels, resulting in subpar performance. Additionally, optimizing partial pixels in each iteration slows down convergence. Recently, [PERSON] proposed a multi-mask strategy, Multi-Masked BSNs (MM-BSN), for self-supervised sRGB image denoising [4]. A multi-mask strategy can effectively break down large noise structures that were previously challenging to handle with single-center masked models. [PERSON] proposed SS-Attention, a novel self-similarity-based self-attention module that integrates SS-Attention into the blind spot network (SS-BSN), enabling training in a fully self-supervised manner [25].