# SnowMamba: Achieving More Precise Snow Removal with Mamba
## 3 Experiments
### Ablation Study  
An ablation study was conducted on the CSD dataset to verify the effectiveness of the proposed algorithm. First, each layer was designed with two SCM Blocks as the basic encoder-decoder structure, referred to as SCM Blockbase, and experiments were performed using the snow removal framework designed in this study, followed by quantitative evaluation. Then, the number of SCM Blocks was adjusted to [2; 2; 9; 2] as per the configuration in the Mamba experiment, while keeping other settings of the snow removal framework unchanged, to conduct further ablation studies. Finally, SEBlock were introduced at corresponding positions, and the activation function was modified to GeLU for the image snow removal task. The quantitative evaluation and visual comparisons of the ablation study are presented in Table 4 and Figure 6, respectively.  
When using the SCM Blockbase as the encoder-decoder framework, the PSNR and SSIM metrics already surpass some state-of-the-art methods. The reduction of residual snow particles in the images demonstrates that this module is well-suited to the snow removal framework designed in this study and exhibits more precise discrimination and removal capabilities for snowflakes. Furthermore, adding more SCM Blocks significantly improves both PSNR and SSIM, indicating the module's strong effectiveness in identifying and removing snowflakes from images. When SEBlocks are incorporated, the PSNR and SSIM metrics further increase, and snow textures in the images are further reduced, resulting in enhanced image quality. This also confirms that the SEBlock positively contributes to the proposed algorithm.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Setting**} & \multicolumn{2}{c}{**Model**} & \multicolumn{2}{c}{**Metric**} \\  & **SCM Blockbase** & **SCM Block** & **SEBlock** & **PSNR** & **SSIM** \\ \hline S1 & ✓ & ✗ & ✗ & 33.44 & 0.95 \\ S2 & ✗ & ✓ & ✗ & 35.61 & 0.96 \\ S3 & ✗ & ✓ & ✓ & 36.56 & 0.97 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ablation study, where “✗” indicates exclusion from training, while “✓” signifies inclusion in training.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method/Dataset**} & \multicolumn{2}{c}{**Snow 100 KS**} & \multicolumn{2}{c}{**Snow 100 KM**} & \multicolumn{2}{c}{**Snow 100 KL**} \\  & **PSNR\(\uparrow\)** & **SSIM\(\uparrow\)** & **PSNR\(\uparrow\)** & **SSIM\(\uparrow\)** & **PSNR\(\uparrow\)** & **SSIM\(\uparrow\)** \\  & **LPIPS\(\downarrow\)** & **DISTS\(\downarrow\)** & **LPIPS\(\downarrow\)** & **DISTS\(\downarrow\)** & **LPIPS\(\downarrow\)** & **DISTS\(\downarrow\)** \\ \hline \multirow{2}{*}{JSTASR} & 28.94 & 0.61 & 28.92 & 0.59 & 28.07 & 0.51 \\  & 0.3650 & 0.1787 & 0.3854 & 0.1881 & 0.4415 & 0.2225 \\ \hline \multirow{2}{*}{HDWCNet} & 28.54 & 0.61 & 21.66 & 0.72 & 28.74 & 0.53 \\  & 0.3278 & 0.1692 & 0.3468 & 0.1779 & 0.4006 & 0.2116 \\ \hline \multirow{2}{*}{DesnowNet} & 32.23 & 0.95 & 30.86 & 0.94 & 27.16 & 0.89 \\  & - & - & - & - & - & - \\ \hline \multirow{2}{*}{TKL} & 34.71 & 0.92 & 33.95 & 0.91 & 31.63 & 0.85 \\  & 0.1188 & 0.0566 & 0.1367 & 0.0665 & 0.1964 & 0.1018 \\ \hline \multirow{2}{*}{LMQFormer} & 36.74 & 0.95 & 35.74 & 0.94 & 32.95 & 0.90 \\  & 0.0751 & 0.0381 & 0.0898 & 0.0460 & 0.1399 & 0.0748 \\ \hline \multirow{2}{*}{**SnowMamba (ours)**} & **38.40** & **0.972** & **36.89** & **0.964** & **33.49** & **0.938** \\  & **0.0667** & **0.0377** & **0.0832** & **0.0459** & **0.1376** & **0.0746** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Quantitative comparisons were made with other state-of-the-art methods on the Snow 100 KL, Snow 100 KM, and Snow 100 KS datasets, with bold indicating the best results.