After the Party: Navigating the Mapping From Color to Ambient Lighting
Florin-Alexandru Vasluianu, Tim Seizinger, Zongwei Wu, Radu Timofte

TL;DR
This paper introduces CL3AN, a large-scale dataset and a novel learning framework inspired by the Retinex model, to improve image restoration under complex colored lighting conditions, addressing limitations of existing methods.
Contribution
The paper presents the first high-resolution dataset for multi-colored lighting image restoration and a new learning approach that effectively disentangles illumination from reflectance.
Findings
Improved robustness under non-homogeneous colored lighting
Enhanced preservation of material-specific reflectance details
Competitive computational efficiency
Abstract
Illumination in practical scenarios is inherently complex, involving colored light sources, occlusions, and diverse material interactions that produce intricate reflectance and shading effects. However, existing methods often oversimplify this challenge by assuming a single light source or uniform, white-balanced lighting, leaving many of these complexities unaddressed. In this paper, we introduce CL3AN, the first large-scale, high-resolution dataset of its kind designed to facilitate the restoration of images captured under multiple Colored Light sources to their Ambient-Normalized counterparts. Through benchmarking, we find that leading approaches often produce artifacts, such as illumination inconsistencies, texture leakage, and color distortion, primarily due to their limited ability to precisely disentangle illumination from reflectance. Motivated by this insight, we achieve such a…
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