babylonpolice.com

Rain in the dark is a common natural phenomenon. Photos captured in such a
condition significantly impact the performance of various nighttime activities,
such as autonomous driving, surveillance systems, and night photography. While
existing methods designed for low-light enhancement or deraining show promising
performance, they have limitations in simultaneously addressing the task of
brightening low light and removing rain. Furthermore, using a cascade approach,
such as ``deraining followed by low-light enhancement'' or vice versa, may lead
to difficult-to-handle rain patterns or excessively blurred and overexposed
images. To overcome these limitations, we propose an end-to-end network called
$L^{2}RIRNet$ which can jointly handle low-light enhancement and deraining. Our
network mainly includes a Pairwise Degradation Feature Vector Extraction
Network (P-Net) and a Restoration Network (R-Net). P-Net can learn degradation
feature vectors on the dark and light areas separately, using contrastive
learning to guide the image restoration process. The R-Net is responsible for
restoring the image. We also introduce an effective Fast Fourier - ResNet
Detail Guidance Module (FFR-DG) that initially guides image restoration using
detail image that do not contain degradation information but focus on texture
detail information. Additionally, we contribute a dataset containing synthetic
and real-world low-light-rainy images. Extensive experiments demonstrate that
our $L^{2}RIRNet$ outperforms existing methods in both synthetic and complex
real-world scenarios.