# CA-BSN: Mural Image Denoising Based on Cross-Attention Blind Spot Network
## 2 Related Works
### Unsupervised Image Denoising  
[PERSON] et al. [21] proposed Noise2 Noise, where the network can use noisy images to learn to transform noisy images into clean images. They held the belief that since the input and output noise is random, going to force the learning of the relationship between the two will have two results using CNN. With fewer training samples, the CNN learns the transition relationship between the two noise patterns. When the number of samples is large enough, since the noise is randomly unpredictable and stands to minimize the loss, the convolution can learn the clean image itself. This method has the disadvantage of requiring various noisy image pairs. Noise2 Noise is only suitable for a part of the cases. Considering this, [PERSON] et al. [22] proposed Noise2 Void, which uses BSN to denoise directly on a single image. The BSN assumes that the pixels of a real image are conditionally correlated, while the noise pixels are independent of each other and are not correlated with each other. A neural network uses this implicit information about a contaminated pixel point to infer the true value of the contaminated portion by looking at the pixel points around it. However, real noise does not necessarily satisfy the assumption that pixels are independent and have zero mean, so this method is less effective in dealing with structured noise. [PERSON] [23] and [PERSON] et al. [24] further used BSN to deal with noise in images; the shortcoming is that the convolution's structure restricts the network model from utilizing the remote information to take full advantage of the global feature. [PERSON] et al. [25] proposed using a conditional adversarial network for adversarial training between generators and discriminators. Still, this method is demanding on the training data, and the model is prone to overfitting. Denoising may not be effective if multiple noise types are present. In order to ease the loss of pixel information caused by BSN denoising, [PERSON] et al. [26] added a branch to Noise2 Void and improved the denoising performance of BSN by introducing non-blind point denoising. However, the method works under the assumption of noise pixel independence and is less effective in removing noise with spatial correlation. [PERSON] et al. [27] proposed CVF-SID to separate the noise component from the clean image; it assumes that the noise space is uncorrelated, which does not match the true noise distribution. [PERSON] et al. [13] used Asymmetric PD (AP) for real-world noise to break the spatial correlation of noise, which destroys the structural noise by separating neighboring pixels into different small-size maps. However, the choice to use a larger PD step size to destroy the larger scale noise can also have some impact on the image details, destroying the texture's coherence [28]. Therefore, maintaining a balance between image denoising and preserving high-frequency information such as textures and edges is an urgent problem.  
Denoising is an indispensable part of a mural images' digitization process. It has better adaptability to the use of unsupervised denoising methods for model training when not having clean mural images as data labels. Therefore, we use a feature extraction network (FEN) to extract global and local features, construct two cross-attention blocks to fuse the information of each stage, and enhance the nonlinear expression ability of the network through the processing of a feed-forward network (FFN). While ensuring the image denoising effect, high-frequency information such as texture is retained as much as possible to reduce the loss of information in the detailed part of the image.