Dual Degradation Representation for Joint Deraining and Low-Light Enhancement in the Dark

Rain in the dark poses significant challenges 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. Cascade approaches...

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Bibliographic Details
Published in:IEEE transactions on circuits and systems for video technology 2024-10, p.1-1
Main Authors: Lin, Xin, Yue, Jingtong, Ding, Sixian, Ren, Chao, Qi, Lu, Yang, Ming-Hsuan
Format: Article
Language:English
Subjects:
Degradation
Detailed image guidance
Dual degradation loss and representation learning
Feature extraction
Frequency-domain analysis
Image enhancement
Image restoration
Low-light-rainy image restoration
Rain
Representation learning
Surveillance
Training
Transformers
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Summary:Rain in the dark poses significant challenges 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. Cascade approaches, like "deraining followed by low-light enhancement" and vice versa, often result in problematic rain patterns or overly blurred and overexposed images. To address these challenges, we introduce a novel two-stage model called L 2 RIRNet, which innovatively integrates low-light enhancement and deraining into a single framework in real-world settings. Our model comprises two key 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, which are constrained by dual degradation loss and have not been discussed in the previous methods. The Restoration Network restores the degraded image using a Fourier Detail Guidance (FDG) module, which focuses on texture details in frequency and spatial domains to inform the restoration process and leverages near-rainless detailed images. 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 synthetic and complex real-world scenarios. All the code and dataset can be found in https://github.com/linxin0/Low_light_rainy.
ISSN:1051-8215
1558-2205
DOI:10.1109/TCSVT.2024.3487849