{"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/dlbi-deep-learning-guided-bayesian-inference","title":"DLBI: Deep learning guided Bayesian inference for structure reconstruction of super-resolution fluorescence microscopy","arxiv_id":"1805.07777","date":"2018-05-20","proceeding":null,"authors":["Yu Li","Fan Xu","Fa Zhang","Pingyong Xu","Mingshu Zhang","Ming Fan","Lihua Li","Xin Gao","Renmin Han"],"abstract":"Super-resolution fluorescence microscopy, with a resolution beyond the\ndiffraction limit of light, has become an indispensable tool to directly\nvisualize biological structures in living cells at a nanometer-scale\nresolution. Despite advances in high-density super-resolution fluorescent\ntechniques, existing methods still have bottlenecks, including extremely long\nexecution time, artificial thinning and thickening of structures, and lack of\nability to capture latent structures. Here we propose a novel deep learning\nguided Bayesian inference approach, DLBI, for the time-series analysis of\nhigh-density fluorescent images. Our method combines the strength of deep\nlearning and statistical inference, where deep learning captures the underlying\ndistribution of the fluorophores that are consistent with the observed\ntime-series fluorescent images by exploring local features and correlation\nalong time-axis, and statistical inference further refines the ultrastructure\nextracted by deep learning and endues physical meaning to the final image.\nComprehensive experimental results on both real and simulated datasets\ndemonstrate that our method provides more accurate and realistic local patch\nand large-field reconstruction than the state-of-the-art method, the 3B\nanalysis, while our method is more than two orders of magnitude faster. The\nmain program is available at https://github.com/lykaust15/DLBI","url_abs":"http://arxiv.org/abs/1805.07777v3","url_pdf":"http://arxiv.org/pdf/1805.07777v3.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":"dlbi-deep-learning-guided-bayesian-inference","repo_url":"https://github.com/lykaust15/DLBI","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"bayesian-inference","task_name":"Bayesian Inference"},{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"super-resolution","task_name":"Super-Resolution"},{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}