Bidirectional Image-Event Guided Low-Light Image Enhancement

• URL: http://arxiv.org/abs/2506.06120v1
• Date: Fri, 06 Jun 2025 14:28:17 GMT
• Title: Bidirectional Image-Event Guided Low-Light Image Enhancement
• Authors: Zhanwen Liu, Huanna Song, Yang Wang, Nan Yang, Shangyu Xie, Yisheng An, Xiangmo Zhao,
• Abstract summary: Under extreme low-light conditions, traditional frame-based cameras, due to their limited dynamic range and temporal resolution, face detail loss and motion blur in captured images.<n>To overcome this bottleneck, researchers have introduced event cameras and proposed event-guided low-light image enhancement algorithms.<n>However, these methods neglect the influence of global low-frequency noise caused by dynamic lighting conditions and local structural discontinuities in sparse event data.
• Score: 18.482432245937247
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Under extreme low-light conditions, traditional frame-based cameras, due to their limited dynamic range and temporal resolution, face detail loss and motion blur in captured images. To overcome this bottleneck, researchers have introduced event cameras and proposed event-guided low-light image enhancement algorithms. However, these methods neglect the influence of global low-frequency noise caused by dynamic lighting conditions and local structural discontinuities in sparse event data. To address these issues, we propose an innovative Bidirectional guided Low-light Image Enhancement framework (BiLIE). Specifically, to mitigate the significant low-frequency noise introduced by global illumination step changes, we introduce the frequency high-pass filtering-based Event Feature Enhancement (EFE) module at the event representation level to suppress the interference of low-frequency information, and preserve and highlight the high-frequency edges.Furthermore, we design a Bidirectional Cross Attention Fusion (BCAF) mechanism to acquire high-frequency structures and edges while suppressing structural discontinuities and local noise introduced by sparse event guidance, thereby generating smoother fused representations.Additionally, considering the poor visual quality and color bias in existing datasets, we provide a new dataset (RELIE), with high-quality ground truth through a reliable enhancement scheme. Extensive experimental results demonstrate that our proposed BiLIE outperforms state-of-the-art methods by 0.96dB in PSNR and 0.03 in LPIPS.

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