# CA-BSN: Mural Image Denoising Based on Cross-Attention Blind Spot Network
## 4 Experiments
### Ablation Study  
In order to validate the impact that each module brings to the denoising of mural images in our method, we designed ablation studies based on two evaluation metrics, PSNR and SSIM, on the mainstream SIDD. We verify the effect of different convolution kernel sizes and masked sizes of masked convolution layers in the masked convolution module (MCM) on model training. The role of the convolution kernel in the masked convolution layer is to extract features, and the mask is mainly used for blind spot mapping in the subsequent network, demonstrating the impact on model performance and the number of parameters through changes in convolution kernel and masked size. The specific details are shown in Table 3.  
\"Masked Conv1\" denotes the masked convolution used to extract local features, \"Masked Conv2\" denotes the masked convolution used to extract global features, and \"Masked Conv3\" denotes the masked convolution used to extract previous features. The size of the convolution kernel affects the extraction of features, the small convolution kernel is suitable for extracting the detailed information of the image, and the large convolution kernel is suitable for extracting the overall information of the image. The larger the size of the convolution kernel, the higher the number of parameters in the computational equation. The size of the mask affects some information in the pixels of the image; when a larger mask is used, the texture information of the image may be lost due to occlusion. The larger the masked size, the less data are involved in the computation. In order to verify the superiority of our designed feature extraction network (FEN) and cross attention network (CAN), we designed an ablation study using the substitution method as shown in Table 4.  
Case (e) is our method, Case (a) is to replace both parts of the feature extraction with global feature extraction using attention mechanism, Case (b) is to replace both parts of the feature extraction with local feature extraction using convolution operation, and Case (c) and Case (d) are to replace the cross-feature fusion part with the common \"concat\" in turn. Table 4 shows the model performance and the amount of computation for different settings. Experiments show that the feature extraction combining the convolution and attention mechanisms is more effective for model training. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Cases** & **FEN** & **CAN** & **PSNR (dB)** & **SSIM** & **FLOPS (G)** & **Params (M)** \\ \hline (a) & NLA & NLA & LGCA & FFCA & 36.80 & 0.912 & 32.8 & 0.94 \\ (b) & DDR & DDR & LGCA & FFCA & 36.52 & 0.898 & 28.3 & 0.82 \\ (c) & DDR & NLA & concat & FFCA & 36.71 & 0.920 & 27.2 & 0.88 \\ (d) & DDR & NLA & LGCA & concat & 36.77 & 0.892 & 27.7 & 0.90 \\ (e) & DDR & NLA & LGCA & FFCA & **36.92** & **0.932** & 30.4 & 0.91 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Ablation study with feature extraction and fusion. The highest value is highlighted in bold for denoising models. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Masked Conv1** & \multicolumn{2}{c}{**Masked Conv2**} & \multicolumn{2}{c}{**Masked Conv3**} & \multirow{2}{*}{**PSNR (dB)**} & \multirow{2}{*}{**SSIM**} & \multirow{2}{*}{**Params (M)**} \\ \hline
**Kernel** & **Masked** & **Kernel** & **Masked** & **Kernel** & **Masked** & \\ \hline
5 \(\times\) 5 & \(1\times 1\) & 5 \(\times\) 5 & \(1\times 1\) & 5 \(\times\) 5 & \(1\times 1\) & 35.50 & 0.926 & 0.94 \\
3 \(\times\) 3 & \(1\times 1\) & 3 \(\times\) 3 & \(1\times 1\) & 5 \(\times\) 5 & \(1\times 1\) & 35.67 & 0.93  
We designed an ablation study to verify the effect of different convolutional layers for the number of \(1\times 1\) convolutional layers for multiple after feed-forward modules (FFMs) in the feed-forward network (FFN).