Towards Image Ambient Lighting Normalization

TL;DR

This paper introduces Ambient Lighting Normalization, a new task addressing complex lighting interactions, along with a large dataset and a novel method, IFBlend, that outperforms existing approaches in diverse lighting conditions.

Contribution

The paper defines the new ALN task, creates the Ambient6K dataset with complex lighting scenarios, and proposes IFBlend, a method that achieves state-of-the-art results without shadow priors.

Findings

IFBlend achieves SOTA scores on Ambient6K.

IFBlend performs competitively on shadow removal benchmarks.

Ambient6K dataset includes complex geometries and multiple light sources.

Abstract

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.