{"ID":2879613,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.16830","arxiv_id":"2508.16830","title":"AIM 2025 Low-light RAW Video Denoising Challenge: Dataset, Methods and Results","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.","short_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 n...","url_abs":"https://arxiv.org/abs/2508.16830","url_pdf":"https://arxiv.org/pdf/2508.16830v1","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\"]","published":"2025-08-22T23:02:21Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"eess.IV\"]","methods":"[]","has_code":false}