# SnowMamba: Achieving More Precise Snow Removal with Mamba
## 2 Methods
### Design of the SnowMamba Framework  
The architecture of the designed SnowMamba snow removal network is shown in Figure 2. First, the network applies a \(3\times 3\) convolution module to the input snowflake image for preliminary processing, extracting detailed information and edge features to lay the foundation for subsequent image processing. Based on this, the snowflake image information processed by the convolution operation is fed into the SE module to further enhance the network's ability to perceive snowflake features. The SE module adaptively adjusts the weights of each channel to strengthen critical snowflake features, thereby improving the overall feature representation capability. To retain more information and enhance the generalization ability of the network, this work adopts GeLU as the activation function in the SE module. Compared with the traditional ReLU activation function, GeLU demonstrates superior performance in nonlinear mapping and output smoothness. Its smooth transition characteristics enable the network to adjust channel weights with greater precision. GeLU effectively captures variations in features across different channels, enhancing the network's sensitivity to subtle differences. This is particularly beneficial for processing complex snowflake images, as it helps to retain more useful feature information. The snowflake and background feature information enhanced by the SE module is fed into the encoder-decoder structure composed of SCM modules for multi-scale feature extraction. The design of this structure aims to process snowflake features at different scales, enabling the network to simultaneously focus on the local details and global background information of the image. In the encoder stage, the network extracts multi-scale snowflake and background features through downsampling, progressively deepening its understanding of the image during the process. In the decoder stage, the network gradually restores the spatial resolution of the image through upsampling to further extract detailed features and background information from the snowflake image. To enable the network to capture complex snowflake features across a global range, the  
Figure 2: The SnowMamba framework includes modules such as SCMblock, SEBlock, and ResBlock. The two subfigures, respectively, show the detailed structure of SEBlock and ResBlock; \(\ominus\) denotes the residual operation. The MRNet network consists of four ResBlocks, two upsampling layers, and three layers of brown residual information. SE module is reintroduced in the decoder to enhance features during the transmission of information across different scales. This process ensures that the network dynamically adjusts channel weights when transferring features between scales, allowing it to learn more precise and effective snowflake mask image features. The multi-scale features obtained from the encoder-decoder are carefully fed into the MRNet for multi-scale feature fusion, enabling the network to learn the background features obscured by snowflakes and assist in reconstructing clear, snow-free background images. As shown in Figure 2, the MRNet architecture is composed of multi-scale feature residuals, ResBlocks, and multiple upsampling stages. First, the snowflake feature information and background feature information of different scales extracted by the encoder-decoder are fused through residual operations to obtain snow-free feature information at various scales. These features are then reconstructed through ResBlocks and multiple upsampling stages, allowing the network to learn detailed local background features. Since the process of restoring images using downsampling and upsampling modules inherently results in information loss, this work incorporates a residual connection between the initial features and the final extracted snowflake image features. This strategy captures more direct background information, mitigating the loss caused by layer-by-layer sampling.