# SnowMamba: Achieving More Precise Snow Removal with Mamba  
[PERSON]  
1 School of Computer Science and Technology, Xinjiang University, Urumqi 830046, China; [EMAIL_ADDRESS] (G.W. ); [EMAIL_ADDRESS] (Y.Z. ); [EMAIL_ADDRESS] (Z.J.) [PERSON]  
1 School of Computer Science and Technology, Xinjiang University, Urumqi 830046, China; [EMAIL_ADDRESS] (G.W. ); [EMAIL_ADDRESS] (Y.Z. ); [EMAIL_ADDRESS] (Z.J.) [PERSON]  
1 School of Computer Science and Technology, Xinjiang University, Urumqi 830046, China; [EMAIL_ADDRESS] (G.W. ); [EMAIL_ADDRESS] (Y.Z. ); [EMAIL_ADDRESS] (Z.J.) [PERSON]  
1 School of Computer Science and Technology, Xinjiang University, Urumqi 830046, China; [EMAIL_ADDRESS] (G.W. ); [EMAIL_ADDRESS] (Y.Z. ); [EMAIL_ADDRESS] (Z.J.) Footnote 1: email: {zhanghang  
dense connections have been applied to more efficient desnowing networks [11; 12; 13]. Although these methods can remove most snowflakes, their ability to recognize snowflakes is limited due to the constraints of long-range feature extraction in CNNs, resulting in residual snowflakes or black shadows in the restored images [9; 10; 12] and, consequently, a decline in image quality. The application of Vision Transformers (ViT) [14] addresses the inherent locality of CNNs by establishing global feature relationships, enabling the effective handling of image-related tasks, and has been widely adopted [15; 16]. Transformers, leveraging the attention mechanism, combine contextual features for the more precise recognition and removal of snowflakes. Snow removal networks based on Transformers [17; 18; 19; 20] demonstrate strong performance advantages. However, existing methods [17; 18] show limited stability across different datasets and exhibit issues such as residual snowflakes and artifacts. Recently, the introduction of Mamba [21; 22; 23] has provided new insights for long-range modeling, showing superior performance over CNNs and Transformers in image segmentation [22] and image restoration [24]. Due to the diversity and complexity of snowflakes, relying solely on local features or long-range modeling is insufficient for accurately identifying and removing snowflakes, reconstructing background information, and producing higher-quality images. Designing more efficient networks that combine local features with long-range modeling for image desnowing remains an open challenge. Therefore, we propose a multi-scale image desnowing network based on Mamba, named SnowMamba, which better captures and integrates long-distance feature relationships among snowflakes, avoiding image quality issues still present in desnowing, as highlighted by [10; 12; 18]. Firstly, we design a four-layer encoder-decoder structure to identify and differentiate between snowflake particles and background information. During the information input and decoding stages, an SE module is introduced to enhance the network's learning of snowflake features. Then, a Multi-Scale Residual Interaction Network (MRNet) is used to learn and reconstruct a clear background image without snow. For the encoder-decoder, a Snow Caption Mamba (SCM) module is designed for the more precise extraction of snowflake and background features. This module combines CNN and Mamba to fully utilize the long-distance feature relationships and local details of snowflakes in the image, improving snow removal and background restoration. The contributions of this paper are summarized as follows:  
* This paper presents a novel multi-scale residual snow removal architecture based on Mamba, marking the first attempt to apply Mamba in the field of snow removal;
* This paper designs the SCM module to combine local and contextual image features, to accurately identify snowflakes and background information, and to assist the network in removing snowflakes and restoring clear images;
* Extensive experiments show that the method proposed in this paper outperforms existing approaches on three major synthetic datasets and real-world datasets, achieving higher-quality snow removal in images.