AIM 2025 challenge on Inverse Tone Mapping Report: Methods and Results
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arXiv
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| Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866908548902944768 |
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| author | Wang, Chao Banterle, Francesco Ren, Bin Timofte, Radu Lu, Xin Peng, Yufeng Ge, Chengjie Sun, Zhijing Zhou, Ziang Li, Zihao Liao, Zishun Kang, Qiyu Fu, Xueyang Zha, Zheng-Jun Sun, Zhijing Wang, Xingbo Liu, Kean Xu, Senyan Qiu, Yang Ding, Yifan Eilertsen, Gabriel Unger, Jonas Wang, Zihao Wu, Ke Pan, Jinshan Liu, Zhen Li, Zhongyang Liu, Shuaicheng Uddin, S. M Nadim |
| author_facet | Wang, Chao Banterle, Francesco Ren, Bin Timofte, Radu Lu, Xin Peng, Yufeng Ge, Chengjie Sun, Zhijing Zhou, Ziang Li, Zihao Liao, Zishun Kang, Qiyu Fu, Xueyang Zha, Zheng-Jun Sun, Zhijing Wang, Xingbo Liu, Kean Xu, Senyan Qiu, Yang Ding, Yifan Eilertsen, Gabriel Unger, Jonas Wang, Zihao Wu, Ke Pan, Jinshan Liu, Zhen Li, Zhongyang Liu, Shuaicheng Uddin, S. M Nadim |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_13479 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AIM 2025 challenge on Inverse Tone Mapping Report: Methods and Results Wang, Chao Banterle, Francesco Ren, Bin Timofte, Radu Lu, Xin Peng, Yufeng Ge, Chengjie Sun, Zhijing Zhou, Ziang Li, Zihao Liao, Zishun Kang, Qiyu Fu, Xueyang Zha, Zheng-Jun Sun, Zhijing Wang, Xingbo Liu, Kean Xu, Senyan Qiu, Yang Ding, Yifan Eilertsen, Gabriel Unger, Jonas Wang, Zihao Wu, Ke Pan, Jinshan Liu, Zhen Li, Zhongyang Liu, Shuaicheng Uddin, S. M Nadim Computer Vision and Pattern Recognition Image and Video Processing 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. |
| title | AIM 2025 challenge on Inverse Tone Mapping Report: Methods and Results |
| topic | Computer Vision and Pattern Recognition Image and Video Processing |
| url | https://arxiv.org/abs/2508.13479 |