WaveFrSnow: Comprehensive perception wavelet transform frequency separation transformer for image snow removal

Types of snow degradation are complex and diverse. Snow removal often requires the construction of sufficient visual representations. Although convolution-based methods perform well in local perception, they struggle to model globally. On the other hand, methods based on self-attention can capture long-range dependencies but often overlook local information and texture details. In this paper, we proposed a hybrid network called WaveFrSnow, aimed at enhancing the performance of single-image snow removal by combining the advantages of convolution and cross-attention. Firstly, we introduced a frequency-separation cross-attention mechanism based on wavelet transform (WaveFrSA) to enhance the global and texture representations of snow removal. Specifically, frequency-separated attention perceives the texture in the high-frequency branch, captures global information in the low-frequency branch, and introduces convolution to obtain local features. In addition, we constructed local representations through efficient convolutional encoder branches. Furthermore, we develop a Multi-Scale Degradation Aggregation (MSDA) module to integrate rich implicit degradation features. Based on the MSDA module, a Degradation Area Restoration (DAR) network is constructed, aiming to achieve high-quality image restoration following the snow removal process. Taken together, comprehensive experimental results on multiple publicly available datasets demonstrate the superiority of the proposed method over the state-of-the-art method. Additionally, the desnowing results effectively improve the accuracy of downstream vision tasks. The code and datasets in this study are available at https://github.com/dxw2000/WaveFrSnow.