AIM 2025 challenge on Inverse Tone Mapping Report: Methods and Results
Chao Wang, Francesco Banterle, Bin Ren, Radu Timofte, Xin Lu, Yufeng Peng, Chengjie Ge, Zhijing Sun, Ziang Zhou, Zihao Li, Zishun Liao, Qiyu Kang, Xueyang Fu, Zheng-Jun Zha, Zhijing Sun, Xingbo Wang, Kean Liu, Senyan Xu, Yang Qiu, Yifan Ding, Gabriel Eilertsen, Jonas Unger, Zihao Wang, Ke Wu, Jinshan Pan, Zhen Liu, Zhongyang Li, Shuaicheng Liu, S. M Nadim Uddin
Published: 2025/8/19
Abstract
This paper presents a comprehensive review of the AIM 2025 Challenge on Inverse Tone Mapping (ITM). The challenge aimed to push forward the development of effective ITM algorithms for HDR image reconstruction from single LDR inputs, focusing on perceptual fidelity and numerical consistency. A total of \textbf{67} participants submitted \textbf{319} valid results, from which the best five teams were selected for detailed analysis. This report consolidates their methodologies and performance, with the lowest PU21-PSNR among the top entries reaching 29.22 dB. The analysis highlights innovative strategies for enhancing HDR reconstruction quality and establishes strong benchmarks to guide future research in inverse tone mapping.