Reconstruction of high dynamic range image from a single low dynamic range image captured by a conventional RGB camera, which suffers from over- or under-exposure, is an ill-posed problem. In contrast, recent neuromorphic cameras like event camera and spike camera can record high dynamic range scenes in the form of intensity maps, but with much lower spatial resolution and no color information. In this article, we propose a hybrid imaging system (denoted as NeurImg) that captures and fuses the visual information from a neuromorphic camera and ordinary images from an RGB camera to reconstruct high-quality high dynamic range images and videos. The proposed NeurImg-HDR+ network consists of specially designed modules, which bridges the domain gaps on resolution, dynamic range, and color representation between two types of sensors and images to reconstruct high-resolution, high dynamic range images and videos. We capture a test dataset of hybrid signals on various HDR scenes using the hybrid camera, and analyze the advantages of the proposed fusing strategy by comparing it to state-of-the-art inverse tone mapping methods and merging two low dynamic range images approaches. Quantitative and qualitative experiments on both synthetic data and real-world scenarios demonstrate the effectiveness of the proposed hybrid high dynamic range imaging system. Code and dataset can be found at: https://github.com/hjynwa/NeurImg-HDR

中文翻译：

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EuroCity Persons：交通场景中人员检测的新基准


从传统 RGB 相机捕获的单个低动态范围图像重建高动态范围图像是一个不适定问题，传统 RGB 相机存在曝光过度或曝光不足的问题。相比之下，最近的神经形态相机（如事件相机和尖峰相机）可以以强度图的形式记录高动态范围场景，但空间分辨率要低得多，并且没有颜色信息。在本文中，我们提出了一种混合成像系统（表示为 NeurImg），该系统捕获并融合来自神经形态相机的视觉信息和来自 RGB 相机的普通图像，以重建高质量的高动态范围图像和视频。所提出的 NeurImg-HDR+ 网络由专门设计的模块组成，它弥合了两种类型的传感器和图像之间在分辨率、动态范围和颜色表示方面的域差距，以重建高分辨率、高动态范围图像和视频。我们使用混合相机捕获各种 HDR 场景上的混合信号的测试数据集，并通过将其与最先进的逆色调映射方法进行比较并合并两种低动态范围图像方法来分析所提出的融合策略的优点。对合成数据和真实场景的定量和定性实验证明了所提出的混合高动态范围成像系统的有效性。代码和数据集可以在以下位置找到：https://github.com/hjynwa/NeurImg-HDR