Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/107154
DC Field | Value | Language |
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dc.contributor | Department of Electrical and Electronic Engineering | - |
dc.contributor | Mainland Development Office | - |
dc.creator | Xiao, Y | en_US |
dc.creator | Zhou, L | en_US |
dc.creator | Chen, W | en_US |
dc.date.accessioned | 2024-06-13T01:04:15Z | - |
dc.date.available | 2024-06-13T01:04:15Z | - |
dc.identifier.isbn | 978-1-7281-5304-9 (Electronic) | en_US |
dc.identifier.isbn | 978-1-7281-5305-6 (Print on Demand(PoD)) | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/107154 | - |
dc.description | 2019 Photonics & Electromagnetics Research Symposium - Fall (PIERS - Fall), 17-20 December 2019, Xiamen, China | en_US |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers | en_US |
dc.rights | © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | en_US |
dc.rights | The following publication Y. Xiao, L. Zhou and W. Chen, "High-Quality Object Reconstruction Based on Ghost Imaging," 2019 Photonics & Electromagnetics Research Symposium - Fall (PIERS - Fall), Xiamen, China, 2019, pp. 2903-2907 is available at https://doi.org/10.1109/PIERS-Fall48861.2019.9021799. | en_US |
dc.title | High-quality object reconstruction based on ghost imaging | en_US |
dc.type | Conference Paper | en_US |
dc.identifier.spage | 2903 | en_US |
dc.identifier.epage | 2907 | en_US |
dc.identifier.doi | 10.1109/PIERS-Fall48861.2019.9021799 | en_US |
dcterms.abstract | Ghost imaging (GI) becomes an attractive research topic in recent years, and has been developed for some years. Correlation algorithm is usually used to reconstruct object in the GI. However, due to a linear relationship between quality of the recovered objects and the number of measurements, it needs a large number of measurements to obtain a satisfied object reconstruction when conventional GI is applied. Although some improved algorithms, e.g., differential GI and normalized GI, are developed, they could still not be feasible for achieving high-quality object reconstruction in some cases. In this paper, a high-quality object reconstruction method is presented for the GI. The method takes advantage of the property of Hadamard transform. For a 2D matrix, after the Hadamard transform is applied to it, the first element of the Hadamard spectrum is equivalent to the sum of all matrix elements. In the measurement process of GI, single-pixel detector collects the total light intensity, i.e., the sum of transmitted light. Hence, the property of Hadamard transform corresponds to the single-pixel measurement process in the GI. As a result, it is possible to utilize the detected single-pixel values as constraints. An algorithm is presented in this paper to reduce the number of measurements dramatically in the GI and simultaneously achieve a high-quality object reconstruction. In the method, the signal-to-noise ratio (SNR) has a nonlinear growth with respect to the number of measurements, and it is different from conventional GI methods. Feasibility and effectiveness of the method are computationally demonstrated. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | In Proceedings of 2019 Photonics & Electromagnetics Research Symposium - Fall (PIERS - Fall), 17-20 December 2019, Xiamen, China, p. 2903-2907 | en_US |
dcterms.issued | 2019 | - |
dc.identifier.scopus | 2-s2.0-85082440900 | - |
dc.relation.conference | Progress in Electromagnetics Research Symposium - Fall [PIERS - FALL] | - |
dc.description.validate | 202403 bckw | - |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | EIE-0269 | - |
dc.description.fundingSource | RGC | en_US |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | National Natural Science Foundation of China (NSFC); Shenzhen Science and Technology Innovation Commission through Basic Research Program | en_US |
dc.description.pubStatus | Published | en_US |
dc.identifier.OPUS | 20258002 | - |
dc.description.oaCategory | Green (AAM) | en_US |
Appears in Collections: | Conference Paper |
Files in This Item:
File | Description | Size | Format | |
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Xiao_High-Quality_Object_Reconstruction.pdf | Pre-Published version | 520.83 kB | Adobe PDF | View/Open |
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