  The intricacy of rainy image contents often leads cutting-edge deraining
models to image degradation including remnant rain, wrongly-removed details,
and distorted appearance. Such degradation is further exacerbated when applying
the models trained on synthetic data to real-world rainy images. We observe two
types of domain gaps between synthetic and real-world rainy images: one exists
in rain streak patterns; the other is the pixel-level appearance of rain-free
images. To bridge the two domain gaps, we propose a semi-supervised
detail-recovery image deraining network (Semi-DRDNet) with dual
sample-augmented contrastive learning. Semi-DRDNet consists of three
sub-networks:i) for removing rain streaks without remnants, we present a
squeeze-and-excitation based rain residual network; ii) for encouraging the
lost details to return, we construct a structure detail context aggregation
based detail repair network; to our knowledge, this is the first time; and iii)
for building efficient contrastive constraints for both rain streaks and clean
backgrounds, we exploit a novel dual sample-augmented contrastive
regularization network.Semi-DRDNet operates smoothly on both synthetic and
real-world rainy data in terms of deraining robustness and detail accuracy.
Comparisons on four datasets including our established Real200 show clear
improvements of Semi-DRDNet over fifteen state-of-the-art methods. Code and
dataset are available at https://github.com/syy-whu/DRD-Net.
