多曝光高动态范围（HDR）成像：像素级与特征级重建方法综述
Multi-exposure HDR Imaging: A Review of Pixel-level and Feature-level Reconstruction Methods
AI总结：
本文对多曝光HDR成像的多曝光融合与重影去除方法进行分类，对比深度学习等方法，总结设计原则、数据集指标，指出瓶颈与未来方向。
AI中文摘要：
多曝光是捕获真实世界高动态范围（HDR）场景的有效方式。然而，由于连续曝光之间存在时间间隔，HDR成像在动态场景中会出现严重的重影伪影。在本文中，我们将HDR成像相关文献分为两个重要主题：多曝光融合（MEF）和重影去除。我们在像素空间和特征空间中研究了基于传统滤波器的方法和数据驱动的方法。对于流行的基于深度学习的方法，我们根据其对齐和融合域提供了细致的分类：像素空间方法通常采用显式运动补偿，例如光流或空间变换器；特征空间方法则利用可变形卷积、注意力机制或潜在表示合并实现隐式对齐。我们在不同的监督设置下对代表性工作进行了比较，并总结了关键设计原则。此外，本综述还总结了常用的数据集和评估指标，讨论了它们在不同输出形式下的适用性。最后，概述了主要瓶颈和未来研究的有前景方向。
英文摘要：
Multi-exposure is an efficient way to capture real-world high-dynamic-range (HDR) scenes. However, HDR imaging suffers from severe ghosting artifacts in dynamic scenes due to the temporal gap between sequential exposures. In this article, we categorize the literature on two important topics on HDR imaging: multi-exposure fusion (MEF) and ghost removal. Conventional filter-based and data-driven methods are studied in pixel space and feature space. For popular deep learning-based approaches, we provide a granular taxonomy based on their alignment and fusion domains: pixel-space methods, which typically employ explicit motion compensation such as optical flow or spatial transformers, and feature-space methods, which leverage implicit alignment through deformable convolutions, attention mechanisms, or latent representation merging. Representative works are compared across different supervision settings, and key design principles are summarized. In addition, this survey summarizes commonly used datasets and evaluation metrics, discussing their applicability under diverse output forms. Finally, major bottlenecks and promising directions for future research are outlined.
