  Rain removal plays an important role in the restoration of degraded images.
Recently, data-driven methods have achieved remarkable success. However, these
approaches neglect that the appearance of rain is often accompanied by low
light conditions, which will further degrade the image quality. Therefore, it
is very indispensable to jointly remove the rain and enhance the light for
real-world rain image restoration. In this paper, we aim to address this
problem from two aspects. First, we proposed a novel entangled network, namely
EMNet, which can remove the rain and enhance illumination in one go.
Specifically, two encoder-decoder networks interact complementary information
through entanglement structure, and parallel rain removal and illumination
enhancement. Considering that the encoder-decoder structure is unreliable in
preserving spatial details, we employ a detail recovery network to restore the
desired fine texture. Second, we present a new synthetic dataset, namely
DarkRain, to boost the development of rain image restoration algorithms in
practical scenarios. DarkRain not only contains different degrees of rain, but
also considers different lighting conditions, and more realistically simulates
the rainfall in the real world. EMNet is extensively evaluated on the proposed
benchmark and achieves state-of-the-art results. In addition, after a simple
transformation, our method outshines existing methods in both rain removal and
low-light image enhancement. The source code and dataset will be made publicly
available later.
