AIM 2025 Low-light RAW Video Denoising Challenge: Dataset, Methods and Results
It addresses the problem of low-light video denoising for smartphone camera applications, but is incremental as it focuses on benchmarking and reviewing existing methods.
The paper reviews a challenge to denoise low-light RAW video by exploiting temporal redundancy under exposure-time constraints, introducing a new benchmark dataset of 756 sequences captured with 14 smartphone sensors across various conditions, with submissions evaluated using PSNR and SSIM metrics.
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.