  Image deraining is a fundamental, yet not well-solved problem in computer
vision and graphics. The traditional image deraining approaches commonly behave
ineffectively in medium and heavy rain removal, while the learning-based ones
lead to image degradations such as the loss of image details, halo artifacts
and/or color distortion. Unlike existing image deraining approaches that lack
the detail-recovery mechanism, we propose an end-to-end detail-recovery image
deraining network (termed a DRD-Net) for single images. We for the first time
introduce two sub-networks with a comprehensive loss function which synergize
to derain and recover the lost details caused by deraining. We have three key
contributions. First, we present a rain residual network to remove rain streaks
from the rainy images, which combines the squeeze-and-excitation (SE) operation
with residual blocks to make full advantage of spatial contextual information.
Second, we design a new connection style block, named structure detail context
aggregation block (SDCAB), which aggregates context feature information and has
a large reception field. Third, benefiting from the SDCAB, we construct a
detail repair network to encourage the lost details to return for eliminating
image degradations. We have validated our approach on four recognized datasets
(three synthetic and one real-world). Both quantitative and qualitative
comparisons show that our approach outperforms the state-of-the-art deraining
methods in terms of the deraining robustness and detail accuracy. The source
code has been available for public evaluation and use on GitHub.
