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; 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

arXiv · 2508.16830

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

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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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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. 2025-08-22. AIM 2025 Low-light RAW Video Denoising Challenge: Dataset, Methods and Results. https://arxiv.org/abs/2508.16830

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