标题： 探索傅里叶先验和事件协作用于低光图像增强 显示英文标题

标题： Exploring Fourier Prior and Event Collaboration for Low-Light Image Enhancement

摘要： 事件相机因其高动态范围和低延迟，为低光图像增强提供了性能优势。 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，捕捉到足够的结构信息。 目前，现有的基于事件的方法直接将帧和事件输入到一个模型中，而没有充分利用模态特有的优势，这限制了它们的性能。 因此，通过分析每种传感模态的作用，增强流程被解耦为两个阶段：可见性恢复和结构细化。 在第一阶段，我们设计了一个具有幅度-相位纠缠的可见性恢复网络，通过重新思考傅里叶空间中幅度和相位分量之间的关系。 在第二阶段，提出了一种具有动态对齐的融合策略，以缓解两种传感模态之间时间分辨率差异引起的时空不匹配，旨在细化由可见性恢复网络增强的图像的结构信息。 此外，我们利用空频插值来模拟具有多种光照、噪声和伪影退化的负样本，从而开发了一种对比损失，鼓励模型学习区分性表示。 实验表明，所提出的方法优于最先进模型。 显示英文摘要

摘要： 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.