解决动态场景下多曝光低动态范围视频重建的鬼影问题
F2HDR: Two-Stage HDR Video Reconstruction via Flow Adapter and Physical Motion Modeling
- 分两阶段重建：先对齐曝光差异，再融合细节
- 在大运动和曝光变化下实现无鬼影高保真输出
- 适合需要高质量动态视频重建的研究与应用
从交替曝光的低动态范围（LDR）帧序列中重建高动态范围（HDR）视频仍面临挑战，尤其在动态场景中，跨曝光不一致和复杂运动导致帧间对齐困难，引发鬼影和细节丢失。现有方法常因对齐不准、特征聚合不佳，在运动主导区域重建质量下降。为此，本文提出F2HDR，一种两阶段HDR视频重建框架，能鲁棒感知帧间运动并恢复复杂动态场景中的精细细节。该框架集成流动适配器以适应通用光流实现跨曝光对齐，物理运动建模识别显著运动区域，并采用运动感知精炼网络聚合互补信息，消除鬼影与噪声。大量实验表明，F2HDR在真实世界HDR视频基准上达到领先性能，可在大运动与曝光变化下生成无鬼影、高保真的结果。
原文摘要 · Abstract (English)
Reconstructing High Dynamic Range (HDR) videos from sequences of alternating-exposure Low Dynamic Range (LDR) frames remains highly challenging, especially under dynamic scenes where cross-exposure inconsistencies and complex motion make inter-frame alignment difficult, leading to ghosting and detail loss. Existing methods often suffer from inaccurate alignment, suboptimal feature aggregation, and degraded reconstruction quality in motion-dominated regions. To address these challenges, we propose F2HDR, a two-stage HDR video reconstruction framework that robustly perceives inter-frame motion and restores fine details in complex dynamic scenarios. The proposed framework integrates a flow adapter that adapts generic optical flow for robust cross-exposure alignment, a physical motion modeling to identify salient motion regions, and a motion-aware refinement network that aggregates complementary information while removing ghosting and noise. Extensive experiments demonstrate that F2HDR achieves state-of-the-art performance on real-world HDR video benchmarks, producing ghost-free and high-fidelity results under large motion and exposure variations.
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