# CA-BSN: Mural Image Denoising Based on Cross-Attention Blind Spot Network
## 4 Experiments
### Comparison Experiment  
In order to verify the feasibility and effectiveness of our proposed CA-BSN, we designed comparison experiments of image denoising. Table 1 demonstrates the comparison results of several methods on the SIDD and DND datasets for the objective evaluation index parameters. Figure 10 shows the visualization results of some of the methods in Table 1 on the DND and SIDD. The methods we compared include non-learning based, supervised denoising, and unsupervised methods. As can be seen from Table 1, RIDNet has achieved impressive performance in denoising. However, our analysis focuses on unsupervised denoising, and although RIDNet's performance is noteworthy, it is not suitable for the denoising of mural images due to the difference in categories and the limitation of the application scenarios, which are worse in the application of mural images. Our method outperforms previous unsupervised representative methods in both SIDD and DND, demonstrating excellent denoising performance. Specifically, our proposed CA-BSN improves the PSNR by 0.95 and 0.15 on the two datasets, and the SSIM also grows by 0.7% and 0.2%, respectively, compared to the AP-BSN algorithm. In Figure 10, we show two images from the DND and four images from the SIDD processed by different denoising models. Figure 10a shows the original noisy image, which we zoomed in locally for a more visual comparison with the other methods. Because of our limited time and equipment resources, we choose CBDNet, which saves more training time and model computation compared to RIDNet, as a comparative method to show the test effect. The supervised learning-based method CBDNet cannot clearly discriminate the image's high frequency details due to its model's characteristics, which leads to the situation that the edge information is prone to be deficient, and the model's generalization  
Figure 9: Partial mural dataset presentation. (**a**) are examples from noisy images, and (**b**) are from ground truth data. ability in different scenes is weak. In the unsupervised denoising model, for the direct observation method using a single noisy image, CVF-SID does not take into account the spatial correlation of the real noise, only considers the separation of the noise from the clean image, and the real noise cannot be removed entirely. Figure 10c shows that this approach can blur image edges. The AP-BSN algorithm simply loses some pixel information using dilated convolution and destroys the texture information of the image using PD with a large step size, as seen in Figure 10d. AP-BSN performs poorly in the detailed parts such as edges. Our proposed CA-BSN algorithm designs spatial correlation and remote dependency into the network, preserving the detailed information as much as possible. According to Figure 10e we can see that compared with other algorithms, the edges of the images processed by our CA-BSN are clearer and show better denoising effect. In order to test the denoising effect of our method on the mural dataset, we compare it with the current more popular unsupervised denoising methods, and the results on the evaluation metrics are shown in Table 2.  
In order to verify the specific performance effect of our proposed method in the process of mural image denoising, we select several images from the mural dataset for effect demonstration and visually compare the different unsupervised methods in Table 2. The results are shown in Figure 11.  
We have chosen three unsupervised methods, Noise2 Void, CVF-SID and AP-BSN, to compare with our method. As can be seen from the figure, compared with the three methods, our denoising method has better performance on the mural image, the color spots in the image are removed more completely, and the texture information, which is not very distinguishable from the surrounding noise, is also well preserved, reducing the loss, and the texture part is shown more clearly. Figure 12 displays the denoising effect of CA-BSN on mural images.