  Heavy rain removal from a single image is the task of simultaneously
eliminating rain streaks and fog, which can dramatically degrade the quality of
captured images. Most existing rain removal methods do not generalize well for
the heavy rain case. In this work, we propose a novel network architecture
consisting of three sub-networks to remove heavy rain from a single image
without estimating rain streaks and fog separately. The first sub-net, a
U-net-based architecture that incorporates our Spatial Channel Attention (SCA)
blocks, extracts global features that provide sufficient contextual information
needed to remove atmospheric distortions caused by rain and fog. The second
sub-net learns the additive residues information, which is useful in removing
rain streak artifacts via our proposed Residual Inception Modules (RIM). The
third sub-net, the multiplicative sub-net, adopts our Channel-attentive
Inception Modules (CIM) and learns the essential brighter local features which
are not effectively extracted in the SCA and additive sub-nets by modulating
the local pixel intensities in the derained images. Our three clean image
results are then combined via an attentive blending block to generate the final
clean image. Our method with SCA, RIM, and CIM significantly outperforms the
previous state-of-the-art single-image deraining methods on the synthetic
datasets, shows considerably cleaner and sharper derained estimates on the real
image datasets. We present extensive experiments and ablation studies
supporting each of our method's contributions on both synthetic and real image
datasets.
