## 2 Related Work  
### Deep Learning Technology for Image Denoising  
Noise is a common interference in any field. In model training, a large amount of noise and data conflict, and redundancy and inconsistency will profoundly affect the model's validity. Therefore, the robustness of the model is crucial. [PERSON]. [16] added a self-screening layer in the model, which effectively resisted noise interference by selecting and understanding data and improved the robustness. [PERSON]. [17] designed a variational auto-encoder (VAE) as a time series data predictor to overcome the noise effects. The image restoration task can be transformed into a linear inverse problem, and the diffusion models, acting as state-of-the-art generative models, use diffusion to increase the noise and gradually transform the empirical data distribution into a simple Gaussian distribution. The core idea is to simulate the approximation of \"denoising\" diffusion as opposed to diffusion noise. Therefore, it often has good performance in multiple image restoration tasks [18; 19; 20]. At present, most unsupervised methods to solve the noise removal problem focus on inefficient iterative methods. In order to make the unsupervised methods more efficient [18], based on the variable input, an unsupervised posterior sampling method (DDRM) (unsupervised posterior sampling method) is proposed. DDRM [18] is a general sampling-based linear inverse problem solver based on unconditional/class conditional diffusion generating models as learned priorities, which can effectively reverse significant noise. There are issues of over-smoothing, mode collapse, and large model footprints in the super-resolution domain. To solve these problems, the single image super-resolution diffusion probabilistic model (SRDiff) [19] was used for the first time in the field of super-resolution, which can generate diversified results with rich details. To establish an extended framework that can be applied to different problems, ref. [20] proposed a generic denoising Markov model to extend denoising diffusion models to general state-spaces. Finally, it is proved that the framework has excellent robustness in a series of problems. Vision Transformer (ViT), using the idea of Transform for reference, divides an image into patches of fixed size and processes patches in a way similar to natural language processing. At present, ViT has made great progress in a number of computer vision fields [21]. ViT also has strong performance in multi-scale context aggregation, and each layer can obtain the global information of the image. Swin Transformer [22] enhances multi-scale feature fusion with the shift window method. However, compared with CNN, ViT lacks the sliding operation of the convolution kernel in the feature graph, resulting in the self-attention inductive bias ability of ViT being weaker than that of CNN [23]. In addition, Transformer features need to be transformed as one-dimensional parameters, resulting in partial differences between images and sequences. The global self-attention in ViT means that each pixel must be compared with all other pixels in the image. However, in the field of image denoising, most of the compared pixels are irrelevant, which leads to redundancy in computational complexity. As such, CNN is better at solving pixel-level tasks than Transformer. In the image-denoising task, CUR Transformer [23] divides the image into non-overlapped windows and establishes a communication mechanism to make up for the above shortcomings. DRFENet combines dilated convolution and skip connection to deal with multi-scale fusion more efficiently, and it has the ability to extract more fine-grained feature information. However, the biggest challenge to finding this noise in images is the issue of how to train a complex nonlinearity model with better fitting due to the increasing complexity of all kinds of pattern recognition tasks.