Abstract
Removing rain streaks from a single image has been
drawing considerable attention as rain streaks can severely
degrade the image quality and affect the performance of existing outdoor vision tasks. While recent CNN-based derainers have reported promising performances, deraining
remains an open problem for two reasons. First, existing
synthesized rain datasets have only limited realism, in terms
of modeling real rain characteristics such as rain shape, direction and intensity. Second, there are no public benchmarks for quantitative comparisons on real rain images,
which makes the current evaluation less objective. The core
challenge is that real world rain/clean image pairs cannot be captured at the same time. In this paper, we address the single image rain removal problem in two ways.
First, we propose a semi-automatic method that incorporates temporal priors and human supervision to generate a
high-quality clean image from each input sequence of real
rain images. Using this method, we construct a large-scale
dataset of ?29.5K rain/rain-free image pairs that covers a
wide range of natural rain scenes. Second, to better cover
the stochastic distribution of real rain streaks, we propose
a novel SPatial Attentive Network (SPANet) to remove rain
streaks in a local-to-global manner. Extensive experiments
demonstrate that our network performs favorably against
the state-of-the-art deraining methods.