Capturing Stable HDR Videos Using a Dual-Camera System

• URL: http://arxiv.org/abs/2507.06593v1
• Date: Wed, 09 Jul 2025 07:09:42 GMT
• Title: Capturing Stable HDR Videos Using a Dual-Camera System
• Authors: Qianyu Zhang, Bolun Zheng, Hangjia Pan, Lingyu Zhu, Zunjie Zhu, Zongpeng Li, Shiqi Wang,
• Abstract summary: In HDR video reconstruction, exposure fluctuations in reference images from alternating exposure methods often result in flickering.<n>We propose a dual-camera system (DCS) for HDR video acquisition, where one camera is assigned to capture consistent reference sequences.<n>We introduce an exposure-adaptive fusion network (EAFNet) to achieve more robust results.
• Score: 19.393435127495668
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In HDR video reconstruction, exposure fluctuations in reference images from alternating exposure methods often result in flickering. To address this issue, we propose a dual-camera system (DCS) for HDR video acquisition, where one camera is assigned to capture consistent reference sequences, while the other is assigned to capture non-reference sequences for information supplementation. To tackle the challenges posed by video data, we introduce an exposure-adaptive fusion network (EAFNet) to achieve more robust results. EAFNet introduced a pre-alignment subnetwork to explore the influence of exposure, selectively emphasizing the valuable features across different exposure levels. Then, the enhanced features are fused by the asymmetric cross-feature fusion subnetwork, which explores reference-dominated attention maps to improve image fusion by aligning cross-scale features and performing cross-feature fusion. Finally, the reconstruction subnetwork adopts a DWT-based multiscale architecture to reduce ghosting artifacts and refine features at different resolutions. Extensive experimental evaluations demonstrate that the proposed method achieves state-of-the-art performance on different datasets, validating the great potential of the DCS in HDR video reconstruction. The codes and data captured by DCS will be available at https://github.com/zqqqyu/DCS.

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