  We develop a new physical model for the rain effect and show that the
well-known atmosphere scattering model (ASM) for the haze effect naturally
emerges as its homogeneous continuous limit. Via depth-aware fusion of
multi-layer rain streaks according to the camera imaging mechanism, the new
model can better capture the sophisticated non-deterministic degradation
patterns commonly seen in real rainy images. We also propose a Densely
Scale-Connected Attentive Network (DSCAN) that is suitable for both deraining
and dehazing tasks. Our design alleviates the bottleneck issue existent in
conventional multi-scale networks and enables more effective information
exchange and aggregation. Extensive experimental results demonstrate that the
proposed DSCAN is able to deliver superior derained/dehazed results on both
synthetic and real images as compared to the state-of-the-art. Moreover, it is
shown that for our DSCAN, the synthetic dataset built using the new physical
model yields better generalization performance on real images in comparison
with the existing datasets based on over-simplified models.
