Reconstructing High Dynamic Range (HDR) video from image sequences captured with alternating exposures is challenging, especially in the presence of large camera or object motion. Existing methods typically align low dynamic range sequences using optical flow or attention mechanism for deghosting. However, they often struggle to handle large complex motions and are computationally expensive. To address these challenges, we propose a robust and efficient flow estimator tailored for real-time HDR video reconstruction, named HDRFlow. HDRFlow has three novel designs: an HDR-domain alignment loss (HALoss), an efficient flow network with a multi-size large kernel (MLK), and a new HDR flow training scheme. The HALoss supervises our flow network to learn an HDR-oriented flow for accurate alignment in saturated and dark regions. The MLK can effectively model large motions at a negligible cost. In addition, we incorporate synthetic data, Sintel, into our training dataset, utilizing both its provided forward flow and backward flow generated by us to supervise our flow network, enhancing our performance in large motion regions. Extensive experiments demonstrate that our HDRFlow outperforms previous methods on standard benchmarks. To the best of our knowledge, HDRFlow is the first real-time HDR video reconstruction method for video sequences captured with alternating exposures, capable of processing 720p resolution inputs at 25ms.

翻译：从交替曝光图像序列中重建高动态范围（HDR）视频极具挑战性，尤其在相机或物体存在大幅度运动时。现有方法通常利用光流或注意力机制对齐低动态范围序列以实现去鬼影，但往往难以处理复杂大运动且计算成本高昂。为解决这些问题，我们提出了一种专为实时HDR视频重建设计的鲁棒高效光流估计器——HDRFlow。HDRFlow包含三项创新设计：HDR域对齐损失（HALoss）、多尺寸大核高效光流网络（MLK）以及新型HDR光流训练策略。HALoss监督光流网络学习面向HDR的光流，实现饱和与暗部区域的精准对齐；MLK能以极低计算开销有效建模大范围运动。此外，我们将合成数据集Sintel纳入训练集，利用其提供的前向光流和我们生成的后向光流共同监督光流网络，显著提升大运动区域的重建性能。大量实验表明，HDRFlow在标准基准测试中优于现有方法。据我们所知，HDRFlow是首个针对交替曝光视频序列的实时HDR视频重建方法，可在25毫秒内处理720p分辨率输入。