High Dynamic Range (HDR) imaging aims to replicate the high visual quality and clarity of real-world scenes. Due to the high costs associated with HDR imaging, the literature offers various data-driven methods for HDR image reconstruction from Low Dynamic Range (LDR) counterparts. A common limitation of these approaches is missing details in regions of the reconstructed HDR images, which are over- or under-exposed in the input LDR images. To this end, we propose a simple and effective method, HistoHDR-Net, to recover the fine details (e.g., color, contrast, saturation, and brightness) of HDR images via a fusion-based approach utilizing histogram-equalized LDR images along with self-attention guidance. Our experiments demonstrate the efficacy of the proposed approach over the state-of-art methods.

翻译：高动态范围（HDR）成像旨在再现真实场景的高视觉质量与清晰度。由于HDR成像的高成本，现有文献提出了多种基于数据驱动的方法，用于从低动态范围（LDR）对应图像中重建HDR图像。这些方法的一个常见缺陷在于，重建HDR图像中对应输入LDR图像过曝或欠曝区域的细节缺失。为此，我们提出了一种简单而有效的方法——HistoHDR-Net，通过利用直方图均衡化的LDR图像并结合自注意力引导的融合策略，恢复HDR图像的精细细节（如颜色、对比度、饱和度和亮度）。实验表明，所提方法相较于现有最优方法具有显著优势。