CompEvent: 复数值事件-RGB融合用于低光照视频增强与去模糊
CompEvent: Complex-valued Event-RGB Fusion for Low-light Video Enhancement and Deblurring
AI总结：
CompEvent通过复数神经网络实现事件数据与RGB帧的全过程融合，提升低光照视频去模糊效果。
摘要：
低光照视频去模糊在夜间监控和自动驾驶等应用中面临重大挑战，由于照明昏暗和长时间曝光。虽然事件相机提供了潜在的解决方案，具有优越的低光照灵敏度和高时间分辨率，但现有融合方法通常采用分阶段策略，限制了其在结合低光照和运动模糊退化方面的有效性。为了解决这个问题，我们提出了CompEvent，一种复数神经网络框架，能够实现事件数据和RGB帧的全面全过程融合，以增强联合恢复。CompEvent有两个核心组件：1）复数时间对齐GRU，利用复数值卷积并通过GRU处理视频和事件流，实现时间对齐和连续融合；2）复数空间-频率学习模块，通过空间和频率域中的统一复数值信号处理，促进深度融合。通过利用复数值神经网络的全面表示能力，CompEvent实现了全过程空间-时间融合，最大化模态间的互补学习，并显著增强了低光照视频去模糊能力。大量实验表明，CompEvent在解决这一具有挑战性的任务方面优于现有最先进方法。
英文摘要：
Low-light video deblurring poses significant challenges in applications like nighttime surveillance and autonomous driving due to dim lighting and long exposures. While event cameras offer potential solutions with superior low-light sensitivity and high temporal resolution, existing fusion methods typically employ staged strategies, limiting their effectiveness against combined low-light and motion blur degradations. To overcome this, we propose CompEvent, a complex neural network framework enabling holistic full-process fusion of event data and RGB frames for enhanced joint restoration. CompEvent features two core components: 1) Complex Temporal Alignment GRU, which utilizes complex-valued convolutions and processes video and event streams iteratively via GRU to achieve temporal alignment and continuous fusion; and 2) Complex Space-Frequency Learning module, which performs unified complex-valued signal processing in both spatial and frequency domains, facilitating deep fusion through spatial structures and system-level characteristics. By leveraging the holistic representation capability of complex-valued neural networks, CompEvent achieves full-process spatiotemporal fusion, maximizes complementary learning between modalities, and significantly strengthens low-light video deblurring capability. Extensive experiments demonstrate that CompEvent outperforms SOTA methods in addressing this challenging task.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
