arXiv:2506.01394eess.IVcs.CV2025-06CVPR被引 22

NTIRE 2025挑战赛推动真实世界图像修复新标准,覆盖低光去噪与细节增强。

NTIRE 2025 the 2nd Restore Any Image Model (RAIM) in the Wild Challenge

  • 分双赛道:低光联合去噪去马赛克、图像细节增强生成
  • 共51支队伍提交超600份结果,顶尖方法获20+专家一致认可
  • 含配对与无配对数据,兼顾量化评估与用户主观体验

本文全面介绍NTIRE 2025年第二届真实世界图像恢复模型(RAIM)挑战赛。该挑战建立了一个涵盖多样场景的真实图像修复新基准,包含有参考与无参考两种情况。参赛者需处理实际拍摄、退化复杂且未知的图像,从感知质量与保真度两方面进行评估。挑战分为两个赛道:(1) 低光联合去噪与去马赛克(JDD)任务,(2) 图像细节增强/生成任务。每个赛道含两个子任务:首个子任务使用配对数据提供真实标签,支持量化评估;第二个子任务处理真实但无配对图像,侧重恢复效率与通过全面用户研究评估的主观质量。挑战共吸引近300名注册者,51支团队提交超过600项成果。表现最优的方法显著推进了图像修复技术前沿,并获得20余位专家评委的一致肯定。第1赛道和第2赛道所用数据集分别位于https://drive.google.com/drive/folders/1Mgqve-yNcE26IIieI8lMIf-25VvZRs_J 和 https://drive.google.com/drive/folders/1UB7nnzLwqDZOwDmD9aT8J0KVg2ag4Qae。官方挑战页面链接为:第1赛道 https://codalab.lisn.upsaclay.fr/competitions/21334#learn_the_details,第2赛道 https://codalab.lisn.upsaclay.fr/competitions/21623#learn_the_details。

原文摘要 · Abstract (English)

In this paper, we present a comprehensive overview of the NTIRE 2025 challenge on the 2nd Restore Any Image Model (RAIM) in the Wild. This challenge established a new benchmark for real-world image restoration, featuring diverse scenarios with and without reference ground truth. Participants were tasked with restoring real-captured images suffering from complex and unknown degradations, where both perceptual quality and fidelity were critically evaluated. The challenge comprised two tracks: (1) the low-light joint denoising and demosaicing (JDD) task, and (2) the image detail enhancement/generation task. Each track included two sub-tasks. The first sub-task involved paired data with available ground truth, enabling quantitative evaluation. The second sub-task dealt with real-world yet unpaired images, emphasizing restoration efficiency and subjective quality assessed through a comprehensive user study. In total, the challenge attracted nearly 300 registrations, with 51 teams submitting more than 600 results. The top-performing methods advanced the state of the art in image restoration and received unanimous recognition from all 20+ expert judges. The datasets used in Track 1 and Track 2 are available at https://drive.google.com/drive/folders/1Mgqve-yNcE26IIieI8lMIf-25VvZRs_J and https://drive.google.com/drive/folders/1UB7nnzLwqDZOwDmD9aT8J0KVg2ag4Qae, respectively. The official challenge pages for Track 1 and Track 2 can be found at https://codalab.lisn.upsaclay.fr/competitions/21334#learn_the_details and https://codalab.lisn.upsaclay.fr/competitions/21623#learn_the_details.

图像修复真实世界低光处理用户评估

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