  Rain in the dark poses a significant challenge to deploying real-world
applications such as autonomous driving, surveillance systems, and night
photography. Existing low-light enhancement or deraining methods struggle to
brighten low-light conditions and remove rain simultaneously. Additionally,
cascade approaches like ``deraining followed by low-light enhancement'' or the
reverse often result in problematic rain patterns or overly blurred and
overexposed images. To address these challenges, we introduce an end-to-end
model called L$^{2}$RIRNet, designed to manage both low-light enhancement and
deraining in real-world settings. Our model features two main components: a
Dual Degradation Representation Network (DDR-Net) and a Restoration Network.
The DDR-Net independently learns degradation representations for luminance
effects in dark areas and rain patterns in light areas, employing dual
degradation loss to guide the training process. The Restoration Network
restores the degraded image using a Fourier Detail Guidance (FDG) module, which
leverages near-rainless detailed images, focusing on texture details in
frequency and spatial domains to inform the restoration process. Furthermore,
we contribute a dataset containing both synthetic and real-world
low-light-rainy images. Extensive experiments demonstrate that our
L$^{2}$RIRNet performs favorably against existing methods in both synthetic and
complex real-world scenarios. All the code and dataset can be found in
\url{https://github.com/linxin0/Low_light_rainy}.
