  Existing raindrop removal datasets have two shortcomings. First, they consist
of images captured by cameras with a focus on the background, leading to the
presence of blurry raindrops. To our knowledge, none of these datasets include
images where the focus is specifically on raindrops, which results in a blurry
background. Second, these datasets predominantly consist of daytime images,
thereby lacking nighttime raindrop scenarios. Consequently, algorithms trained
on these datasets may struggle to perform effectively in raindrop-focused or
nighttime scenarios. The absence of datasets specifically designed for
raindrop-focused and nighttime raindrops constrains research in this area. In
this paper, we introduce a large-scale, real-world raindrop removal dataset
called Raindrop Clarity. Raindrop Clarity comprises 15,186 high-quality
pairs/triplets (raindrops, blur, and background) of images with raindrops and
the corresponding clear background images. There are 5,442 daytime raindrop
images and 9,744 nighttime raindrop images. Specifically, the 5,442 daytime
images include 3,606 raindrop- and 1,836 background-focused images. While the
9,744 nighttime images contain 4,838 raindrop- and 4,906 background-focused
images. Our dataset will enable the community to explore background-focused and
raindrop-focused images, including challenges unique to daytime and nighttime
conditions. Our data and code are available at:
\url{https://github.com/jinyeying/RaindropClarity}
