{"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/high-throughput-high-resolution-registration","title":"High-throughput, high-resolution registration-free generated adversarial network microscopy","arxiv_id":"1801.07330","date":"2018-01-07","proceeding":null,"authors":["Hao Zhang","Xinlin Xie","Chunyu Fang","Yicong Yang","Di Jin","Peng Fei"],"abstract":"We combine generative adversarial network (GAN) with light microscopy to\nachieve deep learning super-resolution under a large field of view (FOV). By\nappropriately adopting prior microscopy data in an adversarial training, the\nneural network can recover a high-resolution, accurate image of new specimen\nfrom its single low-resolution measurement. Its capacity has been broadly\ndemonstrated via imaging various types of samples, such as USAF resolution\ntarget, human pathological slides, fluorescence-labelled fibroblast cells, and\ndeep tissues in transgenic mouse brain, by both wide-field and light-sheet\nmicroscopes. The gigapixel, multi-color reconstruction of these samples\nverifies a successful GAN-based single image super-resolution procedure. We\nalso propose an image degrading model to generate low resolution images for\ntraining, making our approach free from the complex image registration during\ntraining dataset preparation. After a welltrained network being created, this\ndeep learning-based imaging approach is capable of recovering a large FOV (~95\nmm2), high-resolution (~1.7 {\\mu}m) image at high speed (within 1 second),\nwhile not necessarily introducing any changes to the setup of existing\nmicroscopes.","url_abs":"http://arxiv.org/abs/1801.07330v2","url_pdf":"http://arxiv.org/pdf/1801.07330v2.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":"high-throughput-high-resolution-registration","repo_url":"https://github.com/xinDW/RFGANM","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":null,"task_name":"Generative Adversarial Network"},{"task_slug":"image-registration","task_name":"Image Registration"},{"task_slug":"image-super-resolution","task_name":"Image Super-Resolution"},{"task_slug":"super-resolution","task_name":"Super-Resolution"},{"task_slug":"high","task_name":"Vocal Bursts Intensity Prediction"}],"methods":[{"method_slug":"speed","method_name":"SPEED"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}