AI RESEARCH
Dynamic Weight-based Temporal Aggregation for Low-light Video Enhancement Under Extreme Noise
arXiv CS.CV
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ArXi:2510.09450v2 Announce Type: replace Low-light video enhancement (LLVE) is challenging due to noise, low contrast, and color degradation. While learning-based methods enable fast inference, they often fail under heavy real-world noise because they do not sufficiently exploit long-term temporal cues. We propose DWTA-Net, a novel deep-learning recurrent LLVE framework with a recurrent design.