  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.
