  Rain streaks might severely degenerate the performance of video/image
processing tasks. The investigations on rain removal from video or a single
image has thus been attracting much research attention in the field of computer
vision and pattern recognition, and various methods have been proposed against
this task in the recent years. However, there is still not a comprehensive
survey paper to summarize current rain removal methods and fairly compare their
generalization performance, and especially, still not a off-the-shelf toolkit
to accumulate recent representative methods for easy performance comparison and
capability evaluation. Aiming at this meaningful task, in this study we present
a comprehensive review for current rain removal methods for video and a single
image. Specifically, these methods are categorized into model-driven and
data-driven approaches, and more elaborate branches of each approach are
further introduced. Intrinsic capabilities, especially generalization, of
representative state-of-the-art methods of each approach have been evaluated
and analyzed by experiments implemented on synthetic and real data both
visually and quantitatively. Furthermore, we release a comprehensive
repository, including direct links to 74 rain removal papers, source codes of 9
methods for video rain removal and 20 ones for single image rain removal, 19
related project pages, 6 synthetic datasets and 4 real ones, and 4 commonly
used image quality metrics, to facilitate reproduction and performance
comparison of current existing methods for general users. Some limitations and
research issues worthy to be further investigated have also been discussed for
future research of this direction.
