NTIRE 2025 Ambient Lighting Normalization Challenge Report

Florin-Alexandru Vasluianu, Tim Seizinger, Zhuyun Zhou, Zongwei Wu, Radu Timofte, Yuanfei Bao, Xingbo Wang, Xin Lu, Jiarong Yang, Anya Hu, Kunyu Wang, Jie Xiao, Dong Li, Xueyang Fu, Zheng-Jun Zha, Zihao Fan, Xi Wang, Yurui Zhu, Kean Liu, Senyan Xu et al. (36 additional authors not shown)

Abstract: This report presents an overview of the NTIRE 2025 Ambient Lighting Normalization Challenge, a competition designed to advance techniques for improving image consistency under varying lighting conditions. Participants were tasked with developing algorithms capable of normalizing images acquired under various direct lighting systems to ambient lighting equivalents while preserving image quality, detail, and color accuracy. With a total number of 171 participants, the first edition of the challenge resulted in a number of 10 Final Phase submissions, which are part of the challenge benchmark. Conditions such as image restoration fidelity and the perceptual quality of the normalized outputs form the base of the proposed ranking. A user study including subjects with various backgrounds, including professional photographers, is backing the proposed ranking, emphasizing clearly the top-performing solutions. This report outlines the competition framework, dataset composition, evaluation metrics, and performance of different approaches. The top-performing methods leveraged deep learning strategies, using both end-to-end learning techniques and solutions based on iterative refinement. A comparative analysis of submissions highlights the strengths and limitations of each approach, offering insights into the effectiveness of all proposed ambient lighting normalization strategies.

External IDs:dblp:conf/cvpr/VasluianuSZWTBW25