CompEvent: Complex-valued Event-RGB Fusion for Low-light Video Enhancement and Deblurring
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Autonomous DrivingVideo EnhancementSimilar Papers 제목 키워드 기반
Complex Diffusion Maps with $ω$-Parameterized Kernels Revealing Inherent Harmonic Representations
In this paper, we propose Complex Diffusion Maps (CDM), a novel diffusion mapping framework that aims to reveal the dominant complex harmonics of high-dimensional data. Inspired by the local Gaussian kernel relevant to t…
Computational EfficiencyFully Complex-valued Fully Convolutional Multi-feature Fusion Network (FC2MFN) for Building Segmentation of InSAR images
Building segmentation in high-resolution InSAR images is a challenging task that can be useful for large-scale surveillance. Although complex-valued deep learning networks perform better than their real-valued counterpar…
SegmentationSemantic SegmentationAdversarial Audio Synthesis with Complex-valued Polynomial Networks
Time-frequency (TF) representations in audio synthesis have been increasingly modeled with real-valued networks. However, overlooking the complex-valued nature of TF representations can result in suboptimal performance a…
Audio GenerationAudio SynthesisUnified Complex-valued Neural Network: A Magnitude-Phase Computational Model for Event-Driven Neuromorphic Learning
Artificial neural networks (ANN) provide accurate continuous-valued representation, whereas spiking neural networks (SNN) offer event-driven temporal processing, yet both paradigms face limitations when value encoding an…
Object TrackingHyperReal: Complex-Valued Layer Functions For Complex-Valued Scaling Invariance
Complex-valued measurements in MRI and SAR imaging often have complex-valued scaling ambiguity, calling for models that are invariant to complex-valued scaling of pixels. Deep Complex Networks (DCN) extends real-valued a…