TL;DR
This paper introduces a novel event-illumination collaborative framework for low-light image enhancement, utilizing a high-resolution real-world dataset and new modules to improve noise reduction and illumination handling.
Contribution
The paper proposes the EIC-LIE framework with innovative modules and a new dataset, advancing real-world event-based low-light image enhancement techniques.
Findings
Outperforms state-of-the-art methods on five datasets
Achieves up to 1.24dB PSNR improvement
Significantly reduces event noise based on brightness statistics
Abstract
Event-based low-light image enhancement (LIE) methods mainly focus on incorporating high dynamic range (HDR) information from events while overlooking the essential global illumination in images and the inherent noise sensitivity of event signals in real-world scenarios. To address these issues, we propose EIC-LIE, an event-illumination collaborative LIE framework. Concretely, we first design an Event-Illumination Collaborative Interaction (EICI) module, which contains two key processes: forward gathering, which gathers HDR features across varying lighting conditions, and backward injection, which provides complementary content for illumination and event representations. Next, we introduce an Illumination-aware Event Filter (IAEF) that dynamically reduces event noise based on brightness statistics derived from images. Additionally, we build a beam-splitter-based hybrid imaging system to…
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