{"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/end-to-end-denoising-of-dark-burst-images","title":"End-to-End Denoising of Dark Burst Images Using Recurrent Fully Convolutional Networks","arxiv_id":"1904.07483","date":"2019-04-16","proceeding":null,"authors":["Di Zhao","Lan Ma","Songnan Li","Dahai Yu"],"abstract":"When taking photos in dim-light environments, due to the small amount of\nlight entering, the shot images are usually extremely dark, with a great deal\nof noise, and the color cannot reflect real-world color. Under this condition,\nthe traditional methods used for single image denoising have always failed to\nbe effective. One common idea is to take multiple frames of the same scene to\nenhance the signal-to-noise ratio. This paper proposes a recurrent fully\nconvolutional network (RFCN) to process burst photos taken under extremely\nlow-light conditions, and to obtain denoised images with improved brightness.\nOur model maps raw burst images directly to sRGB outputs, either to produce a\nbest image or to generate a multi-frame denoised image sequence. This process\nhas proven to be capable of accomplishing the low-level task of denoising, as\nwell as the high-level task of color correction and enhancement, all of which\nis end-to-end processing through our network. Our method has achieved better\nresults than state-of-the-art methods. In addition, we have applied the model\ntrained by one type of camera without fine-tuning on photos captured by\ndifferent cameras and have obtained similar end-to-end enhancements.","url_abs":"http://arxiv.org/abs/1904.07483v1","url_pdf":"http://arxiv.org/pdf/1904.07483v1.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":[{"paper_slug":"end-to-end-denoising-of-dark-burst-images","repo_url":"https://github.com/z-bingo/Recurrent-Fully-Convolutional-Networks","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"denoising","task_name":"Denoising"},{"task_slug":"image-denoising","task_name":"Image Denoising"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1904.07483","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}