# CA-BSN: Mural Image Denoising Based on Cross-Attention Blind Spot Network
## 5 Conclusions  
In this paper, we propose CA-BSN for denoising mural images, aiming to denoise the image while preserving the texture details of the image. First, we propose a mask convolution module (MCM) containing a parallel three-branch structure that feeds the first two branches into the feature extraction network (FEN) for the image's local and global features, respectively. Then, the two results are subjected to local and global cross-attention fusion, and FFCA is used to fuse the features before and after full text processing. Finally, the denoising results are output after the nonlinear mapping of feed forward module (FFM) and multilayer \(1\times 1\) convolution layers. The texture and edge information of the image are preserved as much as possible while ensuring the denoising performance. Experiments prove that our method outperforms some of the previous methods and demonstrates the excellent results of the CA-BSN model on a large amount of mural image data.  
In the future, we hope that our work can be further researched under the conditions of the existing results to preserve the texture information as much as possible and to implement the methodology into specific applications to try our best to improve the digitization of murals.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Cases** & **LGCA** & **FFCA** & **FFN** & **PSNR (dB)** & **SSIM** \\ \hline (a) & \(\surd\) & \(\times\) & \(\times\) & 32.25 & 0.733 \\ (b) & \(\surd\) & \(\times\) & \(\surd\) & 32.51 & 0.787 \\ (c) & \(\surd\) & \(\surd\) & \(\times\) & 36.42 & 0.894 \\ (d) & \(\surd\) & \(\surd\) & \(\surd\) & **36.92** & **0.932** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Ablation study using different modules. The highest value is highlighted in bold for denoising models.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **PSNR (dB)** & **SSIM** & **Params (M)** \\ \hline \(1\times\) Conv & 35.50 & 0.926 & 0.95 \\ \(2\times\) Conv & 35.67 & **0.932** & 0.94 \\ \(4\times\) Conv & **36.92** & **0.932** & 0.96 \\ \(6\times\) Conv & 36.87 & 0.929 & 0.97 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ablation study with different number of convolutional layers. The highest value is highlighted in bold for denoising models.  
**Author Contributions:** Conceptualization, X.C. and Y.L.; methodology, X.C. and Y.L.; software, X.C.; validation, X.C., Y.L. and S.L.; formal analysis, X.C.; investigation, Y.L.; resources, X.C.; data curation, Y.L. and H.S.; writing--original draft preparation, Y.L.; writing--review and editing, X.C.; visualization, S.L.; supervision, H.Z. and H.S.; project administration, Y.L. and H.Z.; funding acquisition, H.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Funding Project of National Natural Science Foundation of China (No. 61503005) and the Funding Project of Humanities and Social Sciences Foundation of the Ministry of Education in China (No. 22 YJAZH002).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The SIDD dataset used in this study can be obtained from [[http://www.cs.yorku.ca/~kamel/sidd/dataset.php](http://www.cs.yorku.ca/~kamel/sidd/dataset.php)]([http://www.cs.yorku.ca/~kamel/sidd/dataset.php](http://www.cs.yorku.ca/~kamel/sidd/dataset.php)) (accessed on 18 March 2023). The DND dataset in the study was obtained from [[https://noise.visinf.tu-darmstadt.de/](https://noise.visinf.tu-darmstadt.de/)]([https://noise.visinf.tu-darmstadt.de/](https://noise.visinf.tu-darmstadt.de/)) (accessed on 3 April 2023).  
**Conflicts of Interest:** The authors declare no conflicts of interest.