CompEvent: Complex-valued Event-RGB Fusion for Low-light Video Enhancement and Deblurring

• URL: http://arxiv.org/abs/2511.14469v1
• Date: Tue, 18 Nov 2025 13:09:13 GMT
• Title: CompEvent: Complex-valued Event-RGB Fusion for Low-light Video Enhancement and Deblurring
• Authors: Mingchen Zhong, Xin Lu, Dong Li, Senyan Xu, Ruixuan Jiang, Xueyang Fu, Baocai Yin,
• Abstract summary: Low-light deblurring poses significant challenges in applications like nighttime surveillance and autonomous driving due to dim lighting.<n>We propose CompEvent, a complex neural network framework enabling holistic full-process fusion of event data and RGB frames for enhanced joint restoration.
• Score: 78.16703719473078
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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. The code is available at https://github.com/YuXie1/CompEvent.

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