[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119135-en":3,"doc-seo-119135-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119135,8796095462418,"Noah","https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780",8,"Research & Report","Compressive Sensing Enhanced by Machine Learning - Paper","Machine learning is used to enhance the reconstruction quality of a multimode-fiber (MMF) compressive sensing imaging system, addressing limitations in spatial resolution and acquisition speed. A generative adversarial network (GAN) is implemented to learn the sparsity structure of the model and enable compressive reconstructions even when signals are not sparse in a chosen representation basis. The proposed approach improves image quality and noise robustness versus common compressive imaging algorithms. Experiments further validate GAN-enhanced ghost imaging below the diffraction limit at sub-Nyquist acquisition speed through a thin MMF probe, demonstrating strong potential for applications from biomedical imaging to remote sensing.","VU Research Portal  \nCompressive Sensing Enhanced by Machine Learning  \nLi, Wei; Abrashitova, Ksenia; Osnabrugge, Gerwin; Amitonova, Lyubov V.  \npublished in  \n2023 Conference on Lasers and Electro-Optics Europe and European Quantum Electronics Conference (CLEO/Europe-EQEC)  \n2023  \nDOI (link to publisher)  \n10.1109/CLEO/EUROPE-EQEC57999.2023.10231825  \ndocument version  \nPublisher's PDF, also known as Version of record  \ndocument license  \nArticle 25fa Dutch Copyright Act  \nLink to publication in VU Research Portal  \ncitation for published version (APA)  \nLi, W. , Abrashitova, K. , Osnabrugge, G. , & Amitonova, L. V. (2023) . Compressive Sensing Enhanced by Machine Learning. In 2023 Conference on Lasers and Electro-Optics Europe and European Quantum Electronics Conference (CLEO/Europe-EQEC): [Proceedings] Institute of Electrical and Electronics Engineers Inc.. [https://doi.org/10.1109/CLEO/EUROPE-EQEC57999.2023.10231825](https://doi.org/10.1109/CLEO/EUROPE-EQEC57999.2023.10231825)  \nGeneral rights  \nCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.  \n• Users may download and print one copy of any publication from the public portal for the purpose of private study or research.  \n• You may not further distribute the material or use it for any profit-making activity or commercial gain  \n• You may freely distribute the URL identifying the publication in the public portal  \nTake down policy  \nIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.  \nE-mail address:  \n[vuresearchportal.ub@vu.nl](vuresearchportal.ub@vu.nl)  \n[Download date: 26](Download date: 26) . Mar. 2025  \n2023 Conference on Lasers an Europe European Quantum Electronics Conference (CLEO/Europe-EQEC)d E lectro-Optics & | 979-8-3503-4599-5/23/$31.00 ©2023 IEEE | DOI: 10. 1 109/CLEO/ EUROPE-EQEC57999. 2023. 10231825  \nCompressive Sensing Enhanced by Machine Learning  \nWei Li 1 , Ksenia Abrashitova 1 , Gerwin Osnabrugge 1 , Lyubov V. Amitonova 1,2  \n1. Advanced Research Center for Nanolithography (ARCNL), Science Park 106, 1098 XG Amsterdam, The Netherlands  \n2. LaserLaB, Department of Physics and Astronomy, Vrije Universiteit Amsterdam, De Boelelaan 1081, 1081 HV Amsterdam, The  \nNetherlands  \nWe present our work on the using of machine learning to enhance the reconstruction quality of multimode fiber (MMF) based compressive sensing system [1] . MMF represents the ultimate limit in miniaturization of imaging endoscopes [2,3] . However, the spatial resolution and acquisition speed are usually limited in this system [4] . With a data-driven machine learning framework, we can solve both the problems. We implement a generative adversarial network (GAN) to explore the sparsity inherent to the model. This gives the possibility to provide compressive reconstruction images that are not sparse in a representation basis. The proposed method exceeds other widespread compressive imaging algorithms in terms of both image quality and noise robustness. We also experimentally demonstrate GAN enhanced ghost imaging below the diffraction limit at a sub-Nyquist speed through a thin MMF probe. The following Fig. 1 illustrates the idea of how to using GAN to improve the reconstruction quality of the compressive sensing problem. While Fig 1(a) shows the simple setup and Fig 1(b) shows the calculation theory, Fig 1(c) give the examples of the comparison of GAN reconstruction with other traditional sparsity based algorithms, where GAN shows clear advantage. Meanwhile, we also discuss the noise robustness of the methods by introducing artificial noise to both the measurement matrix and the measured signal, where GAN also shows superior behavior. Due to its","cbCaibnL6GAed8Xm","https://ap.wps.com/l/cbCaibnL6GAed8Xm","pdf",3500170,1,2,"English","en",105,"# Compressive sensing with MMF imaging\n## GAN-based reconstruction framework\n## Image quality and noise robustness results\n## Experimental ghost imaging performance","[{\"question\":\"How does machine learning improve reconstruction in the multimode-fiber compressive sensing system?\",\"answer\":\"A data-driven GAN framework is introduced to enhance reconstruction quality, targeting the inherent sparsity of the sensing model and improving results when data is not sparse in a fixed representation basis.\"},{\"question\":\"What advantage does the proposed GAN method provide over traditional compressive imaging algorithms?\",\"answer\":\"The document reports superior image quality and better noise robustness compared with widespread sparsity-based compressive imaging approaches.\"},{\"question\":\"What experimental capabilities are demonstrated for the GAN-enhanced method?\",\"answer\":\"Experiments show GAN-enhanced ghost imaging below the diffraction limit with sub-Nyquist acquisition speed using a thin MMF probe.\"}]","Compressive Sensing Enhanced by Machine Learning - Paper | PDF",1785722620,5,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"compressive-sensing-enhanced-by-machine-learning-paper","",{"@graph":36,"@context":85},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,47,50],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":21},"https://docshare.wps.com/document/","Document",{"item":48,"name":12,"@type":43,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":43,"position":52},"https://docshare.wps.com/document/compressive-sensing-enhanced-by-machine-learning-paper/119135/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-04","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"How does machine learning improve reconstruction in the multimode-fiber compressive sensing system?","Question",{"text":75,"@type":76},"A data-driven GAN framework is introduced to enhance reconstruction quality, targeting the inherent sparsity of the sensing model and improving results when data is not sparse in a fixed representation basis.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What advantage does the proposed GAN method provide over traditional compressive imaging algorithms?",{"text":80,"@type":76},"The document reports superior image quality and better noise robustness compared with widespread sparsity-based compressive imaging approaches.",{"name":82,"@type":73,"acceptedAnswer":83},"What experimental capabilities are demonstrated for the GAN-enhanced method?",{"text":84,"@type":76},"Experiments show GAN-enhanced ghost imaging below the diffraction limit with sub-Nyquist acquisition speed using a thin MMF probe.","https://schema.org",{"og:url":51,"og:type":87,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":89,"canonical":51},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,109,114,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":29,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":29,"slug":137},19,"General","general"]