AIM 2025 challenge on Inverse Tone Mapping Report: Methods and Results

• URL: http://arxiv.org/abs/2508.13479v2
• Date: Sun, 21 Sep 2025 09:45:59 GMT
• Title: AIM 2025 challenge on Inverse Tone Mapping Report: Methods and Results
• Authors: 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,
• Abstract summary: This paper reviews the AIM 2025 Challenge on Inverse Tone Mapping (ITM)<n>The challenge aimed to push forward the development of effective ITM algorithms for HDR image reconstruction from single LDR inputs.<n>This report consolidates their methodologies and performance, with the lowest PU21-PSNR among the top entries reaching 29.22 dB.
• Score: 157.40117175150962
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• NTIRE 2025 Challenge on Efficient Burst HDR and Restoration: Datasets, Methods, and Results [89.9254007598818]
  This paper reviews the NTIRE 2025 Efficient Burst HDR and Restoration Challenge.<n>The challenge is based on a novel RAW multi-frame fusion dataset.<n>The top-performing approach achieved a PSNR of 43.22 dB.
  arXiv  Detail & Related papers  (2025-05-17T17:10:22Z)
• NTIRE 2025 Challenge on Image Super-Resolution ($\times$4): Methods and Results [159.15538432295656]
  The NTIRE 2025 image super-resolution ($times$4) challenge is one of the associated competitions of the 10th NTIRE Workshop at CVPR 2025.<n>The challenge aims to recover high-resolution (HR) images from low-resolution (LR) counterparts generated through bicubic downsampling with a $times$4 scaling factor.<n>A total of 286 participants registered for the competition, with 25 teams submitting valid entries.
  arXiv  Detail & Related papers  (2025-04-20T12:08:22Z)
• The Tenth NTIRE 2025 Efficient Super-Resolution Challenge Report [170.81876816944754]
  The NTIRE 2025 Challenge on Single-Image Efficient Super-Resolution (ESR) aims to advance the development of models that optimize key computational metrics.<n>This paper meticulously analyzes these methods and results, emphasizing groundbreaking advancements in state-of-the-art single-image ESR techniques.
  arXiv  Detail & Related papers  (2025-04-14T20:18:21Z)
• NTIRE 2024 Challenge on Image Super-Resolution ($\ imes$4): Methods and Results [126.78130602974319]
  This paper reviews the NTIRE 2024 challenge on image super-resolution ($times$4) The challenge involves generating corresponding high-resolution (HR) images, magnified by a factor of four, from low-resolution (LR) inputs. The aim of the challenge is to obtain designs/solutions with the most advanced SR performance.
  arXiv  Detail & Related papers  (2024-04-15T13:45:48Z)
• NTIRE 2021 Challenge on High Dynamic Range Imaging: Dataset, Methods and Results [56.932867490888015]
  This paper reviews the first challenge on high-dynamic range imaging that was part of the New Trends in Image Restoration and Enhancement (NTIRE) workshop, held in conjunction with CVPR 2021. The challenge aims at estimating a HDR image from one or multiple respective low-dynamic range (LDR) observations, which might suffer from under- or over-exposed regions and different sources of noise.
  arXiv  Detail & Related papers  (2021-06-02T19:45:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.