AIM 2025 Low-light RAW Video Denoising Challenge: Dataset, Methods and Results

• URL: http://arxiv.org/abs/2508.16830v1
• Date: Fri, 22 Aug 2025 23:02:21 GMT
• Title: AIM 2025 Low-light RAW Video Denoising Challenge: Dataset, Methods and Results
• Authors: Alexander Yakovenko, George Chakvetadze, Ilya Khrapov, Maksim Zhelezov, Dmitry Vatolin, Radu Timofte, Youngjin Oh, Junhyeong Kwon, Junyoung Park, Nam Ik Cho, Senyan Xu, Ruixuan Jiang, Long Peng, Xueyang Fu, Zheng-Jun Zha, Xiaoping Peng, Hansen Feng, Zhanyi Tie, Ziming Xia, Lizhi Wang,
• Abstract summary: This paper reviews the AIM 2025 (Advances in Image Manipulation) Low-Light RAW Video Denoising Challenge.<n>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.<n>We introduce a new benchmark of 756 ten-frame sequences captured with 14 smartphone camera sensors across nine conditions.
• Score: 118.03343690763597
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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