  Most deraining works focus on rain streaks removal but they cannot deal
adequately with heavy rain images. In heavy rain, streaks are strongly visible,
dense rain accumulation or rain veiling effect significantly washes out the
image, further scenes are relatively more blurry, etc. In this paper, we
propose a novel method to address these problems. We put forth a 2-stage
network: a physics-based backbone followed by a depth-guided GAN refinement.
The first stage estimates the rain streaks, the transmission, and the
atmospheric light governed by the underlying physics. To tease out these
components more reliably, a guided filtering framework is used to decompose the
image into its low- and high-frequency components. This filtering is guided by
a rain-free residue image --- its content is used to set the passbands for the
two channels in a spatially-variant manner so that the background details do
not get mixed up with the rain-streaks. For the second stage, the refinement
stage, we put forth a depth-guided GAN to recover the background details failed
to be retrieved by the first stage, as well as correcting artefacts introduced
by that stage. We have evaluated our method against the state of the art
methods. Extensive experiments show that our method outperforms them on real
rain image data, recovering visually clean images with good details.
