# CA-BSN: Mural Image Denoising Based on Cross-Attention Blind Spot Network
## 1 Introduction  
As an important part of traditional Chinese cultural heritage and one of the oldest forms of painting, mural painting is often painted on buildings or stone walls. It reflects the political, economic, literary, artistic and technological development of the society at that time. It is valuable to historical research [1]. However, due to the natural environment and human factors, after hundreds of years, the mural paintings had problems such as mottled images, chipped walls, and alkali returning to the clay layer, which had a serious impact on the research [2; 3; 4]. The processing of mural images has been slow because of the massive engineering, restricted technology, rare talent, and limited resources. It can take a few months to a year or two to process a mural image [5; 6]. Mural images are exposed to air for a long time, and there are typical problems such as fuzzy edges, unclear outlines, noise or color spots are obvious; that is why it is necessary to deal with the problem of noise pollution in the processing of mural images. Images that are affected by equipment or the external environment, or that have color spots and noise, are classified as noisy images. Image denoising refers to recovering a clean or noise-free image from a noisy image. Image denoising is fundamental research in low-level vision [7]. Noise can greatly degrade the image quality, and the denoising effect directly affects other subsequent processing steps [8]. The use of an image denoising method in the digital processing of mural images can eliminate any extraneous noise and spots present in mural images. Additionally, this technique may provide a groundwork for subsequent image processing operations. The traditional image denoising methods mainly include mean filtering [9], median filtering [10] and wavelet transform [11], which are insufficient because they have poorrobustness in the real world and cannot accurately and efficiently remove noise from images [12]. With the development of deep learning, image denoising algorithms based on deep learning have been widely used due to their efficient and convenient characteristics and have become an effective solution to deal with image denoising. Noise is not isolated in pixels in real scenes. There is spatial correlation between most pixel points. In a recent study, AP-BSN [13] takes into account the connection between noisy pixels, and the study proposes to combine PD and BSN to match noisy images in real scenes more closely. AP-BSN first uses downsampling to decompose the noisy image into multiple sub-images, destroys the spatial correlation of noise, and then utilizes the correlation of the surrounding pixels to map out the original pixels under the noisy pixels. However, the presence of larger-scale noise means that if we do not consider the texture information but only focus on how to destroy the spatial noise, drastically destroying the spatial correlation of noise at the same time will also cause a certain degree of damage to the texture information of image, which will add a certain degree of difficulty to the process of the mural images. To this end, we propose a feature extraction network (FEN) to extract global and local feature of the mural images, respectively, and we use two cross-attention blocks for fusion and information interaction between the features, focusing on retaining texture information while denoising. We evaluate this on the mainstream noisy image datasets Smartphone Image Denoising Dataset (SIDD) [14], Darmstadt Noise Dataset (DND) [15], and our homemade mutual noise image dataset. In order to show the experimental effect better, we design comparative experiments. Our method shows better results compared with several representative image denoising methods, not only performing effective denoising, but also retaining more edge and texture information. We summarize our contributions as follows:  
1.