Compared to other severe weather image restoration tasks, single image desnowing is a more challenging task. This is mainly due to the diversity and irregularity of snow shape, which makes it extremely difficult to restore images in snowy scenes. Moreover, snow particles also have a veiling effect similar to haze or mist. Although current works can effectively remove snow particles with various shapes, they also bring distortion to the restored image. To address these issues, we propose a novel single image desnowing network called Star-Net. First, we design a Star type Skip Connection (SSC) to establish information channels for all different scale features, which can deal with the complex shape of snow particles.Second, we present a Multi-Stage Interactive Transformer (MIT) as the base module of Star-Net, which is designed to better understand snow particle shapes and to address image distortion by explicitly modeling a variety of important image recovery features. Finally, we propose a Degenerate Filter Module (DFM) to filter the snow particle and snow fog residual in the SSC on the spatial and channel domains. Extensive experiments show that our Star-Net achieves state-of-the-art snow removal performances on three standard snow removal datasets and retains the original sharpness of the images.

翻译：相较于其他恶劣天气图像复原任务，单幅图像去雪更具挑战性。这主要源于雪花的形态多样性与不规则性，使得雪景图像恢复极为困难。此外，雪粒还会产生类似雾霾的遮挡效应。现有方法虽能有效去除不同形态的雪粒，但会导致复原图像出现失真。为解决这些问题，我们提出了一种新型单幅图像去雪网络——Star-Net。首先，我们设计了星型跳跃连接（SSC）来建立跨尺度特征的信息通道，以应对雪粒的复杂形态。其次，我们提出了多阶段交互式Transformer（MIT）作为Star-Net的基础模块，通过显式建模多种关键图像复原特征，以更精准地理解雪粒形态并解决图像失真问题。最后，我们设计了退化过滤模块（DFM），在空间域和通道域上对SSC中的雪粒与雪雾残留进行过滤。大量实验表明，Star-Net在三个标准去雪数据集上取得了最优的去雪性能，同时保持了图像的原始清晰度。