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}.

翻译：暗光环境下的雨景对自动驾驶、监控系统和夜间摄影等实际应用的部署构成了重大挑战。现有的低光增强或去雨方法难以同时提升暗光条件和去除雨纹。此外，诸如"先去雨后低光增强"或相反顺序的级联方法往往会导致问题性的雨纹残留，或产生过度模糊和过曝的图像。为解决这些挑战，我们提出了一种名为L$^{2}$RIRNet的端到端模型，旨在实现在真实场景中同时进行低光增强和去雨。我们的模型包含两个主要组件：双重退化表征网络（DDR-Net）和复原网络。DDR-Net通过双退化损失指导训练过程，独立学习暗区亮度效应和亮区雨纹模式的退化表征。复原网络采用傅里叶细节引导（FDG）模块来恢复退化图像，该模块利用近无雨的细节图像，在频域和空间域聚焦纹理细节以指导复原过程。此外，我们构建了一个包含合成与真实世界低光雨景图像的数据集。大量实验表明，我们的L$^{2}$RIRNet在合成和复杂真实场景中均优于现有方法。所有代码和数据集均可在\url{https://github.com/linxin0/Low_light_rainy}获取。