The range of real-world scene luminance is larger than the capture capability of many digital camera sensors which leads to details being lost in captured images, most typically in bright regions. Inverse tone mapping attempts to boost these captured Standard Dynamic Range (SDR) images back to High Dynamic Range (HDR) by creating a mapping that linearizes the well exposed values from the SDR image, and provides a luminance boost to the clipped content. However, in most cases, the details in the clipped regions cannot be recovered or estimated. In this paper, we present a novel inverse tone mapping approach for mapping SDR images to HDR that generates lost details in clipped regions through a semantic-aware diffusion based inpainting approach. Our method proposes two major contributions - first, we propose to use a semantic graph to guide SDR diffusion based inpainting in masked regions in a saturated image. Second, drawing inspiration from traditional HDR imaging and bracketing methods, we propose a principled formulation to lift the SDR inpainted regions to HDR that is compatible with generative inpainting methods. Results show that our method demonstrates superior performance across different datasets on objective metrics, and subjective experiments show that the proposed method matches (and in most cases outperforms) state-of-art inverse tone mapping operators in terms of objective metrics and outperforms them for visual fidelity.

翻译：现实世界场景的亮度范围超出了许多数码相机传感器的捕获能力，这导致捕获图像中的细节丢失，最常见于高亮区域。逆色调映射试图通过建立一种映射，将标准动态范围（SDR）图像中曝光良好的值线性化，并对裁剪区域的内容进行亮度提升，从而将这些捕获的SDR图像增强至高动态范围（HDR）。然而，在大多数情况下，裁剪区域的细节无法被恢复或估计。本文提出了一种新颖的逆色调映射方法，用于将SDR图像映射至HDR，该方法通过一种基于语义感知扩散的图像修复方法，在裁剪区域生成丢失的细节。我们的方法提出了两个主要贡献：首先，我们提出使用语义图来指导基于SDR扩散的、对饱和图像中掩码区域的修复。其次，受传统HDR成像和包围曝光方法的启发，我们提出了一种原则性公式，用于将SDR修复区域提升至HDR，该公式与生成式修复方法兼容。结果表明，我们的方法在不同数据集上的客观指标均表现出优越性能，主观实验显示，所提方法在客观指标上与最先进的逆色调映射算子相当（且在大多数情况下优于它们），并在视觉保真度方面优于现有方法。