Low-Light Image Enhancement using Event-Based Illumination Estimation

• URL: http://arxiv.org/abs/2504.09379v1
• Date: Sun, 13 Apr 2025 00:01:33 GMT
• Title: Low-Light Image Enhancement using Event-Based Illumination Estimation
• Authors: Lei Sun, Yuhan Bao, Jiajun Zhai, Jingyun Liang, Yulun Zhang, Kaiwei Wang, Danda Pani Paudel, Luc Van Gool,
• Abstract summary: Low-light image enhancement (LLIE) aims to improve the visibility of images captured in poorly lit environments.<n>This paper opens a new avenue from the perspective of estimating the illumination using ''temporal-mapping'' events.<n>We construct a beam-splitter setup and collect EvLowLight dataset that includes images, temporal-mapping events, and motion events.
• Score: 83.81648559951684
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Low-light image enhancement (LLIE) aims to improve the visibility of images captured in poorly lit environments. Prevalent event-based solutions primarily utilize events triggered by motion, i.e., ''motion events'' to strengthen only the edge texture, while leaving the high dynamic range and excellent low-light responsiveness of event cameras largely unexplored. This paper instead opens a new avenue from the perspective of estimating the illumination using ''temporal-mapping'' events, i.e., by converting the timestamps of events triggered by a transmittance modulation into brightness values. The resulting fine-grained illumination cues facilitate a more effective decomposition and enhancement of the reflectance component in low-light images through the proposed Illumination-aided Reflectance Enhancement module. Furthermore, the degradation model of temporal-mapping events under low-light condition is investigated for realistic training data synthesizing. To address the lack of datasets under this regime, we construct a beam-splitter setup and collect EvLowLight dataset that includes images, temporal-mapping events, and motion events. Extensive experiments across 5 synthetic datasets and our real-world EvLowLight dataset substantiate that the devised pipeline, dubbed RetinEV, excels in producing well-illuminated, high dynamic range images, outperforming previous state-of-the-art event-based methods by up to 6.62 dB, while maintaining an efficient inference speed of 35.6 frame-per-second on a 640X480 image.

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