14款手机摄像头在低光下拍的视频去噪，挑战赛结果出炉。
AIM 2025 Low-light RAW Video Denoising Challenge: Dataset, Methods and Results
- 利用时间冗余和传感器特性，对低光RAW视频逐帧去噪。
- 756段10帧序列，覆盖9种光照与曝光组合，有高信噪比参考。
- 适合做低光视频增强、手机影像算法研究者参考。
本文回顾了AIM 2025（图像处理进展）低光RAW视频去噪挑战赛。任务是在帧率限制下，利用时间冗余对低光RAW视频进行去噪，并适应传感器特有的信号依赖噪声。我们构建了一个新基准数据集，包含756段10帧序列，由14款智能手机相机在9种条件下拍摄（光照：1/5/10 lx；曝光：1/24、1/60、1/120秒），高信噪比参考通过快速连拍平均获得。参赛者需处理线性RAW序列，输出去噪后的第10帧，保持Bayer格式。评估在私有测试集上使用全参考指标PSNR与SSIM，最终排名基于各指标得分的平均秩。本文详述数据集、挑战规程及提交方法。
原文摘要 · Abstract (English)
This paper reviews the AIM 2025 (Advances in Image Manipulation) Low-Light RAW Video Denoising Challenge. The task is to develop methods that denoise low-light RAW video by exploiting temporal redundancy while operating under exposure-time limits imposed by frame rate and adapting to sensor-specific, signal-dependent noise. We introduce a new benchmark of 756 ten-frame sequences captured with 14 smartphone camera sensors across nine conditions (illumination: 1/5/10 lx; exposure: 1/24, 1/60, 1/120 s), with high-SNR references obtained via burst averaging. Participants process linear RAW sequences and output the denoised 10th frame while preserving the Bayer pattern. Submissions are evaluated on a private test set using full-reference PSNR and SSIM, with final ranking given by the mean of per-metric ranks. This report describes the dataset, challenge protocol, and submitted approaches.
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