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arxiv: 2508.16830 · v1 · pith:MJEOFEAD · submitted 2025-08-22 · cs.CV · eess.IV

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

Alexander Yakovenko , George Chakvetadze , Ilya Khrapov , Maksim Zhelezov , Dmitry Vatolin , Radu Timofte , Youngjin Oh , Junhyeong Kwon
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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
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classification cs.CV eess.IV
keywords challengelow-lightvideodatasetdenoisingframemethodssequences
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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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