Abstract
The event camera, benefiting from its high dynamic range and low latency, provides performance gain for low-light image enhancement. Unlike frame-based cameras, it records intensity changes with extremely high temporal resolution, capturing sufficient structure information. Currently, existing event-based methods feed a frame and events directly into a single model without fully exploiting modality-specific advantages, which limits their performance. Therefore, by analyzing the role of each sensing modality, the enhancement pipeline is decoupled into two stages: visibility restoration and structure refinement. In the first stage, we design a visibility restoration network with amplitude-phase entanglement by rethinking the relationship between amplitude and phase components in Fourier space. In the second stage, a fusion strategy with dynamic alignment is proposed to mitigate the spatial mismatch caused by the temporal resolution discrepancy between two sensing modalities, aiming to refine the structure information of the image enhanced by the visibility restoration network. In addition, we utilize spatial-frequency interpolation to simulate negative samples with diverse illumination, noise and artifact degradations, thereby developing a contrastive loss that encourages the model to learn discriminative representations. Experiments demonstrate that the proposed method outperforms state-of-the-art models.
Abstract (translated)
事件相机由于其高动态范围和低延迟，在弱光图像增强方面表现出性能优势。与基于帧的相机不同，它以极高的时间分辨率记录强度变化，并捕获足够的结构信息。然而，现有的基于事件的方法直接将帧和事件输入到单一模型中，而没有充分利用每种模式特有的优点，这限制了它们的表现。 因此，通过分析每个感测模态的作用，增强管道被分解为两个阶段：可见性恢复与结构细化。在第一个阶段，我们设计了一个利用傅立叶空间中振幅和相位分量之间关系的重新思考的可见性恢复网络，并将其集成到一个称为“振幅-相位纠缠”的架构中。 在第二个阶段，为了缓解由于两种感测模式的时间分辨率差异造成的空间不匹配问题，我们提出了一种具有动态对齐策略的融合方法。这种方法旨在细化由可见性恢复网络增强的图像中的结构信息。 此外，我们使用空间频谱插值来模拟多种光照条件、噪声和瑕疵退化的负样本，从而开发出一种对比损失函数，鼓励模型学习更具判别性的表示形式。 实验表明，所提出的方法优于现有的最先进的模型。
URL
https://arxiv.org/abs/2508.00308
