  Images captured in snowy days suffer from noticeable degradation of scene
visibility, which degenerates the performance of current vision-based
intelligent systems. Removing snow from images thus is an important topic in
computer vision. In this paper, we propose a Deep Dense Multi-Scale Network
(\textbf{DDMSNet}) for snow removal by exploiting semantic and geometric
priors. As images captured in outdoor often share similar scenes and their
visibility varies with depth from camera, such semantic and geometric
information provides a strong prior for snowy image restoration. We incorporate
the semantic and geometric maps as input and learn the semantic-aware and
geometry-aware representation to remove snow. In particular, we first create a
coarse network to remove snow from the input images. Then, the coarsely
desnowed images are fed into another network to obtain the semantic and
geometric labels. Finally, we design a DDMSNet to learn semantic-aware and
geometry-aware representation via a self-attention mechanism to produce the
final clean images. Experiments evaluated on public synthetic and real-world
snowy images verify the superiority of the proposed method, offering better
results both quantitatively and qualitatively.
