将单图HDR重建拆解为明暗与颜色两步，提升真实感细节还原。
Intrinsic Single-Image HDR Reconstruction
- 基于物理的内在域建模，分离明暗与颜色重建任务
- 在多种照片上实现更准确的高动态范围还原
- 适合图像增强、摄影后期等需要细节恢复的应用
普通相机的低动态范围（LDR）无法捕捉自然场景中的丰富对比度，导致过曝像素丢失色彩与细节。从单张LDR图像重建场景中真实的高动态范围（HDR）亮度是计算摄影与真实显示的重要任务。该任务需利用场景上下文推断丢失信息，要求神经网络理解高层几何与光照线索，这对数据驱动算法生成高精度、高分辨率结果构成挑战。本文提出一种基于物理启发的内在域重构方法，将问题分解为两个更简单的子任务：在阴影域扩展动态范围，以及在反照率域恢复丢失的色彩细节。通过分别训练网络处理这两个部分，显著提升了在各类照片上的重建表现。
原文摘要 · Abstract (English)
The low dynamic range (LDR) of common cameras fails to capture the rich contrast in natural scenes, resulting in loss of color and details in saturated pixels. Reconstructing the high dynamic range (HDR) of luminance present in the scene from single LDR photographs is an important task with many applications in computational photography and realistic display of images. The HDR reconstruction task aims to infer the lost details using the context present in the scene, requiring neural networks to understand high-level geometric and illumination cues. This makes it challenging for data-driven algorithms to generate accurate and high-resolution results. In this work, we introduce a physically-inspired remodeling of the HDR reconstruction problem in the intrinsic domain. The intrinsic model allows us to train separate networks to extend the dynamic range in the shading domain and to recover lost color details in the albedo domain. We show that dividing the problem into two simpler sub-tasks improves performance in a wide variety of photographs.
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