Zhenming Yang, Chenyu Yuan, Akhtar Munir, Siyuan Ge, Chunfang Wang
{"title":"基于鬼影成像和深度卷积神经网络加强动态目标重建和跟踪","authors":"Zhenming Yang, Chenyu Yuan, Akhtar Munir, Siyuan Ge, Chunfang Wang","doi":"10.1016/j.optcom.2024.131224","DOIUrl":null,"url":null,"abstract":"<div><div>Ghost imaging requires a large amount of sampling data, which limits its applications in the study of dynamic objects. Here, we propose an imaging technique based on deep convolutional neural networks (SaDunet) that can be used to examine the dynamics of target objects. By replacing the traditional correlation imaging reconstruction approach with SaDunet, the ability to recover high-quality images at low sampling rates is enhanced. The motion process of the target object is decomposed into multiple motion frames, and then each frame is imaged separately. Experiments show that the reconstructed image of the target object obtained by this scheme is of high quality, contains almost no noise, and accurately reflects the motion behavior of the target object.</div></div>","PeriodicalId":19586,"journal":{"name":"Optics Communications","volume":null,"pages":null},"PeriodicalIF":2.2000,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Enhancing dynamic target reconstruction and tracking based on ghost imaging and deep convolutional neural networks\",\"authors\":\"Zhenming Yang, Chenyu Yuan, Akhtar Munir, Siyuan Ge, Chunfang Wang\",\"doi\":\"10.1016/j.optcom.2024.131224\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Ghost imaging requires a large amount of sampling data, which limits its applications in the study of dynamic objects. Here, we propose an imaging technique based on deep convolutional neural networks (SaDunet) that can be used to examine the dynamics of target objects. By replacing the traditional correlation imaging reconstruction approach with SaDunet, the ability to recover high-quality images at low sampling rates is enhanced. The motion process of the target object is decomposed into multiple motion frames, and then each frame is imaged separately. Experiments show that the reconstructed image of the target object obtained by this scheme is of high quality, contains almost no noise, and accurately reflects the motion behavior of the target object.</div></div>\",\"PeriodicalId\":19586,\"journal\":{\"name\":\"Optics Communications\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.2000,\"publicationDate\":\"2024-10-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Optics Communications\",\"FirstCategoryId\":\"101\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0030401824009611\",\"RegionNum\":3,\"RegionCategory\":\"物理与天体物理\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"OPTICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Optics Communications","FirstCategoryId":"101","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0030401824009611","RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"OPTICS","Score":null,"Total":0}
Enhancing dynamic target reconstruction and tracking based on ghost imaging and deep convolutional neural networks
Ghost imaging requires a large amount of sampling data, which limits its applications in the study of dynamic objects. Here, we propose an imaging technique based on deep convolutional neural networks (SaDunet) that can be used to examine the dynamics of target objects. By replacing the traditional correlation imaging reconstruction approach with SaDunet, the ability to recover high-quality images at low sampling rates is enhanced. The motion process of the target object is decomposed into multiple motion frames, and then each frame is imaged separately. Experiments show that the reconstructed image of the target object obtained by this scheme is of high quality, contains almost no noise, and accurately reflects the motion behavior of the target object.
期刊介绍:
Optics Communications invites original and timely contributions containing new results in various fields of optics and photonics. The journal considers theoretical and experimental research in areas ranging from the fundamental properties of light to technological applications. Topics covered include classical and quantum optics, optical physics and light-matter interactions, lasers, imaging, guided-wave optics and optical information processing. Manuscripts should offer clear evidence of novelty and significance. Papers concentrating on mathematical and computational issues, with limited connection to optics, are not suitable for publication in the Journal. Similarly, small technical advances, or papers concerned only with engineering applications or issues of materials science fall outside the journal scope.