Long-Short Exposure Fusion With Event Data For Low-Light Video Enhancement

Traditional frame-based imaging systems face trade-offs especially under low-light conditions: long-exposure frames have high signal-to-noise ratio (SNR) but suffer from motion blur and low frame rate, while short-exposure frames are sharp but suffer from noise. Event cameras can complement these modalities by capturing brightness changes and exposure-invariant information at very high temporal resolution. We propose a novel multi-modal framework for lowlight enhancement by integrating information from long-exposure, short-exposure, and event data. We introduce an enhanced lightup mechanism to improve short-exposure frames using illumination from long-exposure frames, and an SNR-guided fusion strategy for noise suppression and detail preservation. We use event information to improve feature selection, especially in the low SNR regions. Experimental results on public datasets demonstrate that our method can effectively handle noise and motion blur. We obtain 14dB improvement in PSNR over state-of-the-art methods based on conventional frames and 4dB improvement over methods based on frames and events.