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Image restoration under severe weather is a challenging task. Most of thepast works focused on removing rain and haze phenomena in images. However, snowis also an extremely common atmospheric phenomenon that will seriously affectthe performance of high-level computer vision tasks, such as object detectionand semantic segmentation. Recently, some methods have been proposed for snowremoving, and most methods deal with snow images directly as the optimizationobject. However, the distribution of snow location and shape is complex.Therefore, failure to detect snowflakes / snow streak effectively will affectsnow removing and limit the model performance. To solve these issues, wepropose a Snow Mask Guided Adaptive Residual Network (SMGARN). Specifically,SMGARN consists of three parts, Mask-Net, Guidance-Fusion Network (GF-Net), andReconstruct-Net. Firstly, we build a Mask-Net with Self-pixel Attention (SA)and Cross-pixel Attention (CA) to capture the features of snowflakes andaccurately localized the location of the snow, thus predicting an accurate snowmask. Secondly, the predicted snow mask is sent into the specially designedGF-Net to adaptively guide the model to remove snow. Finally, an efficientReconstruct-Net is used to remove the veiling effect and correct the image toreconstruct the final snow-free image. Extensive experiments show that ourSMGARN numerically outperforms all existing snow removal methods, and thereconstructed images are clearer in visual contrast. All codes will beavailable.

Snow Mask Guided Adaptive Residual Network for Image Snow Removal