{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/dancing-under-the-stars-video-denoising-in","title":"Dancing under the stars: video denoising in starlight","arxiv_id":"2204.04210","date":"2022-04-08","proceeding":"CVPR 2022 1","authors":["Kristina Monakhova","Stephan R. Richter","Laura Waller","Vladlen Koltun"],"abstract":"Imaging in low light is extremely challenging due to low photon counts. Using sensitive CMOS cameras, it is currently possible to take videos at night under moonlight (0.05-0.3 lux illumination). In this paper, we demonstrate photorealistic video under starlight (no moon present, $<$0.001 lux) for the first time. To enable this, we develop a GAN-tuned physics-based noise model to more accurately represent camera noise at the lowest light levels. Using this noise model, we train a video denoiser using a combination of simulated noisy video clips and real noisy still images. We capture a 5-10 fps video dataset with significant motion at approximately 0.6-0.7 millilux with no active illumination. Comparing against alternative methods, we achieve improved video quality at the lowest light levels, demonstrating photorealistic video denoising in starlight for the first time.","url_abs":"https://arxiv.org/abs/2204.04210v1","url_pdf":"https://arxiv.org/pdf/2204.04210v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"denoising","task_name":"Denoising"},{"task_slug":"image-denoising","task_name":"Image Denoising"},{"task_slug":"video-denoising","task_name":"Video Denoising"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/image-denoising-on-eld-sonya7s2-x100","task":"Image Denoising","dataset":"ELD SonyA7S2 x100","model":"Starlight","rank_in_archive_order":8,"of":9,"metrics":{"PSNR (Raw)":"43.80","SSIM (Raw)":"0.936"},"uses_additional_data":false},{"leaderboard":"/sota/image-denoising-on-eld-sonya7s2-x200","task":"Image Denoising","dataset":"ELD SonyA7S2 x200","model":"Starlight","rank_in_archive_order":8,"of":10,"metrics":{"PSNR (Raw)":"40.86","SSIM (Raw)":"0.884"},"uses_additional_data":false},{"leaderboard":"/sota/image-denoising-on-sid-sonya7s2-x250","task":"Image Denoising","dataset":"SID SonyA7S2 x250","model":"Starlight","rank_in_archive_order":9,"of":10,"metrics":{"PSNR (Raw)":"36.25","SSIM (Raw)":"0.858"},"uses_additional_data":false},{"leaderboard":"/sota/image-denoising-on-sid-x100","task":"Image Denoising","dataset":"SID x100","model":"Starlight","rank_in_archive_order":7,"of":8,"metrics":{"PSNR (Raw)":"40.47","SSIM":"0.926"},"uses_additional_data":false},{"leaderboard":"/sota/image-denoising-on-sid-x300","task":"Image Denoising","dataset":"SID x300","model":"Starlight","rank_in_archive_order":7,"of":8,"metrics":{"PSNR (Raw)":"32.99","SSIM":"0.780"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2204.04210","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}