  In winter scenes, the degradation of images taken under snow can be pretty
complex, where the spatial distribution of snowy degradation is varied from
image to image. Recent methods adopt deep neural networks to directly recover
clean scenes from snowy images. However, due to the paradox caused by the
variation of complex snowy degradation, achieving reliable High-Definition
image desnowing performance in real time is a considerable challenge. We
develop a novel Efficient Pyramid Network with asymmetrical encoder-decoder
architecture for real-time HD image desnowing. The general idea of our proposed
network is to utilize the multi-scale feature flow fully and implicitly mine
clean cues from features. Compared with previous state-of-the-art desnowing
methods, our approach achieves a better complexity-performance trade-off and
effectively handles the processing difficulties of HD and Ultra-HD images.
  The extensive experiments on three large-scale image desnowing datasets
demonstrate that our method surpasses all state-of-the-art approaches by a
large margin both quantitatively and qualitatively, boosting the PSNR metric
from 31.76 dB to 34.10 dB on the CSD test dataset and from 28.29 dB to 30.87 dB
on the SRRS test dataset.
