Lighting normalization is a crucial but underexplored restoration task with broad applications. However, existing works often simplify this task within the context of shadow removal, limiting the light sources to one and oversimplifying the scene, thus excluding complex self-shadows and restricting surface classes to smooth ones. Although promising, such simplifications hinder generalizability to more realistic settings encountered in daily use. In this paper, we propose a new challenging task termed Ambient Lighting Normalization (ALN), which enables the study of interactions between shadows, unifying image restoration and shadow removal in a broader context. To address the lack of appropriate datasets for ALN, we introduce the large-scale high-resolution dataset Ambient6K, comprising samples obtained from multiple light sources and including self-shadows resulting from complex geometries, which is the first of its kind. For benchmarking, we select various mainstream methods and rigorously evaluate them on Ambient6K. Additionally, we propose IFBlend, a novel strong baseline that maximizes Image-Frequency joint entropy to selectively restore local areas under different lighting conditions, without relying on shadow localization priors. Experiments show that IFBlend achieves SOTA scores on Ambient6K and exhibits competitive performance on conventional shadow removal benchmarks compared to shadow-specific models with mask priors. The dataset, benchmark, and code are available at https://github.com/fvasluianu97/IFBlend.

翻译：光照归一化是一项关键但尚未充分探索的修复任务，具有广泛的应用前景。然而，现有研究通常将这一任务简化为阴影去除问题，限制光源数量为单一光源并过度简化场景，从而排除了复杂的自阴影，并将表面类别限制为光滑表面。尽管这些简化方法颇具前景，但阻碍了其向日常应用中更真实场景的泛化能力。本文提出一项名为环境光照归一化（ALN）的新挑战性任务，该任务能够研究阴影间的相互作用，在更广泛的背景下统一图像修复与阴影去除。为解决ALN缺乏合适数据集的问题，我们引入了大规模高分辨率数据集Ambient6K，该数据集首次包含来自多光源的样本，并涵盖了因复杂几何形状产生的自阴影。为进行基准测试，我们选取多种主流方法并在Ambient6K上进行了严格评估。此外，我们提出IFBlend这一新颖的强基线模型，通过最大化图像-频率联合熵，在不依赖阴影定位先验的条件下选择性恢复不同光照条件下的局部区域。实验表明，IFBlend在Ambient6K上取得了最优性能，并在传统阴影去除基准测试中展现出与采用掩码先验的阴影特定模型相竞争的优异表现。数据集、基准测试及代码已开源：https://github.com/fvasluianu97/IFBlend 。