Towards Image Ambient Lighting Normalization
Florin-Alexandru Vasluianu Tim Seizinger Zongwei Wu Rakesh Ranjan Radu Timofte
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.