  Single image deraining (SIDR) often suffers from over/under deraining due to
the nonuniformity of rain densities and the variety of raindrop scales. In this
paper, we propose a \textbf{\it co}ntinuous \textbf{\it de}nsity guided network
(CODE-Net) for SIDR. Particularly, it is composed of { a rain
{\color{black}streak} extractor and a denoiser}, where the convolutional sparse
coding (CSC) is exploited to filter out noises from the extracted rain streaks.
Inspired by the reweighted iterative soft-threshold for CSC, we address the
problem of continuous rain density estimation by learning the weights with
channel attention blocks from sparse codes. We further {\color{black}develop} a
multiscale strategy to depict rain streaks appearing at different scales.
Experiments on synthetic and real-world data demonstrate the superiority of our
methods over recent {\color{black}state of the arts}, in terms of both
quantitative and qualitative results. Additionally, instead of quantizing rain
density with several levels, our CODE-Net can provide continuous-valued
estimations of rain densities, which is more desirable in real applications.
