# SnowMamba: Achieving More Precise Snow Removal with Mamba
## 3 Experiments
### Quantitative Evaluation  
To demonstrate the effectiveness of the proposed algorithm, a quantitative comparison was conducted with other state-of-the-art algorithms on three major benchmark datasets, including single-image snow removal algorithms [7; 9; 10; 12; 18] and severe-weather image restoration algorithms [17; 29; 30; 31]. During the comparison, if the compared methods provided reproducible code, we reproduced the methods and obtained the results. If no code was provided, we referred to the results presented in the respective papers or other widely recognized experimental results for comparison. The comparison results are shown in Table 2. Compared with other state-of-the-art methods, the proposed method achieves the best results across three benchmark datasets. In particular, on the SNOW100K and CSD datasets, the PSNR and SSIM metrics exhibit outstanding performance. Compared to the second-best method, LMQFormer, the PSNR of the proposed algorithm improves by 1.12 dB and 2.03 dB on the SNOW100K and CSD datasets, respectively. The SSIM improves by 0.028 and 0.011, respectively. This fully demonstrates the superiority and stability of the proposed algorithm.  
To validate the generalization capability of the proposed algorithm, this paper further verifies its performance by randomly selecting 2000 images from the L, M, and S datasets of Snow100K. As shown in Table 3 (where \(\uparrow\) indicates higher metric values correspond to better image quality closer to real images and \(\downarrow\) denotes lower values indicate superior restoration quality closer to practical images), the proposed algorithm achieves state-of-the-art results across various image evaluation metrics in datasets with different snowfall conditions. Compared to the second-best method, all metrics, including PSNR, SSIM, LPIPS, and DISTS, show improvements. Notably, the enhancements in PSNR and SSIM are particularly significant: PSNR demonstrates improvements exceeding 1 dB across all datasets, while SSIM shows gains of 0.022, 0.024, and 0.038, respectively, in datasets with snow particles ordered from small to large sizes, compared to the second-most advanced algorithm. These results comprehensively demonstrate the stability and superiority of the proposed algorithm.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Type**} & \multirow{2}{*}{**Method/Dataset**} & \multicolumn{2}{c}{**Snow100K(2000)**} & \multicolumn{2}{c}{**SRRS(2000)**} & \multicolumn{2}{c}{**CSD(2000)**} \\  & & **PSNR** & **SSIM** & **PSNR** & **SSIM** & **PSNR** & **SSIM** \\ \hline \multirow{4}{*}{Desnowing Task} & DesnowNet(TIP’2018) & 30.80 & 0.92 & 20.38 & 0.84 & 20.13 & 0.81 \\  & JSTASR(ECCV’2020) & 23.12 & 0.86 & 25.82 & 0.89 & 28.42 & 0.69 \\  & HDCWNet(ICCV’2021) & 21.23 & 0.73 & 27.78 & 0.92 & 31.80 & 0.90 \\  & SMGARN(CYIU’2023) & 31.92 & 0.93 & 29.14 & 0.94 & 31.93 & 0.95 \\  & LMQFormer(TCSVT’2023) & 35.14 & 0.93 & 31.05 & 0.95 & 34.53 & 0.96 \\ \hline \multirow{4}{*}{Adverse Weather} & All in One(CVPR’2020) & 26.07 & 0.88 & 24.98 & 0.88 & 26.31 & 0.87 \\  & TransWeather(CVPR’2022) & 31.82 & 0.93 & 28.29 & 0.92 & 31.76 & 0.93 \\  & DAN-Net(Front.Comput’2024) & 32.48 & 0.96 & 29.34 & 0.95 & 30.82 & 0.95 \\  & TKL(CVPR’2022) & 31.27 & 0.90 & 29.37 & 0.93 & 33.01 & 0.93 \\ \hline \hline \multicolumn{2}{c}{OUR(SnowMamba)} & **36.26** & **0.958** & **32.99** & **0.968** & **36.56** & **0.977** \\ \hline \hline \end{tabular}
\end{table}
Table 2: CSD, Snow100K, and SRRS datasets were quantitatively evaluated, with bolded black indicating the best results.