  Single image deraining (SID) in real scenarios attracts increasing attention
in recent years. Due to the difficulty in obtaining real-world rainy/clean
image pairs, previous real datasets suffer from low-resolution images,
homogeneous rain streaks, limited background variation, and even misalignment
of image pairs, resulting in incomprehensive evaluation of SID methods. To
address these issues, we establish a new high-quality dataset named
RealRain-1k, consisting of $1,120$ high-resolution paired clean and rainy
images with low- and high-density rain streaks, respectively. Images in
RealRain-1k are automatically generated from a large number of real-world rainy
video clips through a simple yet effective rain density-controllable filtering
method, and have good properties of high image resolution, background
diversity, rain streaks variety, and strict spatial alignment. RealRain-1k also
provides abundant rain streak layers as a byproduct, enabling us to build a
large-scale synthetic dataset named SynRain-13k by pasting the rain streak
layers on abundant natural images. Based on them and existing datasets, we
benchmark more than 10 representative SID methods on three tracks: (1) fully
supervised learning on RealRain-1k, (2) domain generalization to real datasets,
and (3) syn-to-real transfer learning. The experimental results (1) show the
difference of representative methods in image restoration performance and model
complexity, (2) validate the significance of the proposed datasets for model
generalization, and (3) provide useful insights on the superiority of learning
from diverse domains and shed lights on the future research on real-world SID.
The datasets will be released at https://github.com/hiker-lw/RealRain-1k
