High dynamic range (HDR) images capture much more intensity levels than standard ones. Current methods predominantly generate HDR images from 8-bit low dynamic range (LDR) sRGB images that have been degraded by the camera processing pipeline. However, it becomes a formidable task to retrieve extremely high dynamic range scenes from such limited bit-depth data. Unlike existing methods, the core idea of this work is to incorporate more informative Raw sensor data to generate HDR images, aiming to recover scene information in hard regions (the darkest and brightest areas of an HDR scene). To this end, we propose a model tailor-made for Raw images, harnessing the unique features of Raw data to facilitate the Raw-to-HDR mapping. Specifically, we learn exposure masks to separate the hard and easy regions of a high dynamic scene. Then, we introduce two important guidances, dual intensity guidance, which guides less informative channels with more informative ones, and global spatial guidance, which extrapolates scene specifics over an extended spatial domain. To verify our Raw-to-HDR approach, we collect a large Raw/HDR paired dataset for both training and testing. Our empirical evaluations validate the superiority of the proposed Raw-to-HDR reconstruction model, as well as our newly captured dataset in the experiments.

翻译：高动态范围（HDR）图像能够捕捉比标准图像更多的强度层级。现有方法通常从经过相机处理流水线降质的8位低动态范围（LDR）sRGB图像生成HDR图像。然而，从如此有限位深的数据中恢复极高动态范围的场景是一项艰巨任务。不同于现有方法，本文的核心思想是利用信息更丰富的原始传感器数据生成HDR图像，旨在恢复场景中困难区域（HDR场景中最暗和最亮区域）的信息。为此，我们提出一种专门为原始图像设计的模型，利用原始数据的独特特性促进原始到HDR的映射。具体地，我们学习曝光掩码以分离高动态场景中的困难区域与容易区域。随后，我们引入两种重要引导：双强度引导，利用信息更丰富的通道引导信息较少的通道；以及全局空间引导，在扩展的空间域上推断场景细节。为验证我们的原始到HDR方法，我们收集了一个大规模的原始/HDR配对数据集用于训练和测试。实验中的经验评估验证了所提出的原始到HDR重建模型以及新采集数据集的优越性。