挑战真实场景低光RAW图像去噪，推动高效实用的去噪模型发展。
AIM 2025 Challenge on Real-World RAW Image Denoising
- 基于五款DSLR相机拍摄的真实低光噪声图像构建评估基准。
- 综合使用PSNR、SSIM、LPIPS等全参考与非参考指标评分。
- 适合关注图像修复、自动驾驶夜间视觉的研究者和开发者。
我们推出了AIM 2025真实世界RAW图像去噪挑战赛，旨在推动基于数据合成的高效且有效的去噪技术发展。竞赛依托新建立的评估基准，包含在野外使用五种不同DSLR相机捕获的具有挑战性的低光噪声图像。参赛者需开发新型噪声合成流程、网络架构和训练方法，以在不同相机型号上实现高性能表现。优胜者由多项性能指标综合评定，包括全参考指标（PSNR、SSIM、LPIPS）和非参考指标（ARNIQA、TOPIQ）。该竞赛致力于推动基于合成数据训练的、具备相机无关性的低光RAW图像去噪模型的鲁棒性与实用性，契合数字摄影的快速发展。我们预计竞赛成果将影响图像修复、夜间自动驾驶等多个领域。
原文摘要 · Abstract (English)
We introduce the AIM 2025 Real-World RAW Image Denoising Challenge, aiming to advance efficient and effective denoising techniques grounded in data synthesis. The competition is built upon a newly established evaluation benchmark featuring challenging low-light noisy images captured in the wild using five different DSLR cameras. Participants are tasked with developing novel noise synthesis pipelines, network architectures, and training methodologies to achieve high performance across different camera models. Winners are determined based on a combination of performance metrics, including full-reference measures (PSNR, SSIM, LPIPS), and non-reference ones (ARNIQA, TOPIQ). By pushing the boundaries of camera-agnostic low-light RAW image denoising trained on synthetic data, the competition promotes the development of robust and practical models aligned with the rapid progress in digital photography. We expect the competition outcomes to influence multiple domains, from image restoration to night-time autonomous driving.
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