Adaptive differential correspondence imaging based on sorting technique
We develop an adaptive differential correspondence imaging (CI) method using a sorting technique. Different from the conventional CI schemes, the bucket detector signals (BDS) are first processed by a differential technique, and then sorted in a descending (or ascending) order. Subsequently, accordi...
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Veröffentlicht in: | AIP advances 2017-04, Vol.7 (4), p.045121-045121-9 |
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creator | Wu, Heng Zhang, Xianmin Shan, Yilin He, Zhenya Li, Hai Luo, Chunling |
description | We develop an adaptive differential correspondence imaging (CI) method using a sorting technique. Different from the conventional CI schemes, the bucket detector signals (BDS) are first processed by a differential technique, and then sorted in a descending (or ascending) order. Subsequently, according to the front and last several frames of the sorted BDS, the positive and negative subsets (PNS) are created by selecting the relative frames from the reference detector signals. Finally, the object image is recovered from the PNS. Besides, an adaptive method based on two-step iteration is designed to select the optimum number of frames. To verify the proposed method, a single-detector computational ghost imaging (GI) setup is constructed. We experimentally and numerically compare the performance of the proposed method with different GI algorithms. The results show that our method can improve the reconstruction quality and reduce the computation cost by using fewer measurement data. |
doi_str_mv | 10.1063/1.4982733 |
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Different from the conventional CI schemes, the bucket detector signals (BDS) are first processed by a differential technique, and then sorted in a descending (or ascending) order. Subsequently, according to the front and last several frames of the sorted BDS, the positive and negative subsets (PNS) are created by selecting the relative frames from the reference detector signals. Finally, the object image is recovered from the PNS. Besides, an adaptive method based on two-step iteration is designed to select the optimum number of frames. To verify the proposed method, a single-detector computational ghost imaging (GI) setup is constructed. We experimentally and numerically compare the performance of the proposed method with different GI algorithms. The results show that our method can improve the reconstruction quality and reduce the computation cost by using fewer measurement data.</description><identifier>ISSN: 2158-3226</identifier><identifier>EISSN: 2158-3226</identifier><identifier>DOI: 10.1063/1.4982733</identifier><identifier>CODEN: AAIDBI</identifier><language>eng</language><publisher>Melville: American Institute of Physics</publisher><subject>Image reconstruction ; Iterative methods ; Sensors ; Signal processing</subject><ispartof>AIP advances, 2017-04, Vol.7 (4), p.045121-045121-9</ispartof><rights>Author(s)</rights><rights>2017 Author(s). 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Different from the conventional CI schemes, the bucket detector signals (BDS) are first processed by a differential technique, and then sorted in a descending (or ascending) order. Subsequently, according to the front and last several frames of the sorted BDS, the positive and negative subsets (PNS) are created by selecting the relative frames from the reference detector signals. Finally, the object image is recovered from the PNS. Besides, an adaptive method based on two-step iteration is designed to select the optimum number of frames. To verify the proposed method, a single-detector computational ghost imaging (GI) setup is constructed. We experimentally and numerically compare the performance of the proposed method with different GI algorithms. The results show that our method can improve the reconstruction quality and reduce the computation cost by using fewer measurement data.</description><subject>Image reconstruction</subject><subject>Iterative methods</subject><subject>Sensors</subject><subject>Signal processing</subject><issn>2158-3226</issn><issn>2158-3226</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>DOA</sourceid><recordid>eNp9kE1LAzEQhoMoWGoP_oMFTwpb8727x1K0Fgpe9Byy-agpNVmTVPDfm7pFPDmXGYaHd953ALhGcI4gJ_doTrsWN4ScgQlGrK0Jxvz8z3wJZintYCnaIdjSCVgttByy-zSVdtaaaHx2cl-pEKNJQ_DaeGUq9y63zm-rXiajq-CrFGI-LrJRb959HMwVuLByn8zs1Kfg9fHhZflUb55X6-ViUyvSkVxb1mDdYttIrSixFHHYcYYwVtrypseYMMU5Y4gqqGSjDDIttKbR1nYtL8mmYD3q6iB3YojFWfwSQTrxswhxK2SxpvZGwHIK9rihlCIKIZaqLaF7JJXlvDG0aN2MWkMMJULKYhcO0Rf7AiNMGaSEsULdjpSKIaVo7O9VBMXx7QKJ09sLezeySbksswv-H_gbs5CAAQ</recordid><startdate>201704</startdate><enddate>201704</enddate><creator>Wu, Heng</creator><creator>Zhang, Xianmin</creator><creator>Shan, Yilin</creator><creator>He, Zhenya</creator><creator>Li, Hai</creator><creator>Luo, Chunling</creator><general>American Institute of Physics</general><general>AIP Publishing LLC</general><scope>AJDQP</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>8FD</scope><scope>H8D</scope><scope>L7M</scope><scope>DOA</scope></search><sort><creationdate>201704</creationdate><title>Adaptive differential correspondence imaging based on sorting technique</title><author>Wu, Heng ; Zhang, Xianmin ; Shan, Yilin ; He, Zhenya ; Li, Hai ; Luo, Chunling</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c393t-f572d82f7adc43f4160965122cdf67b2235c665514c0ca7ce1e80fe7dff986733</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Image reconstruction</topic><topic>Iterative methods</topic><topic>Sensors</topic><topic>Signal processing</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wu, Heng</creatorcontrib><creatorcontrib>Zhang, Xianmin</creatorcontrib><creatorcontrib>Shan, Yilin</creatorcontrib><creatorcontrib>He, Zhenya</creatorcontrib><creatorcontrib>Li, Hai</creatorcontrib><creatorcontrib>Luo, Chunling</creatorcontrib><collection>AIP Open Access Journals</collection><collection>CrossRef</collection><collection>Technology Research Database</collection><collection>Aerospace Database</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>AIP advances</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wu, Heng</au><au>Zhang, Xianmin</au><au>Shan, Yilin</au><au>He, Zhenya</au><au>Li, Hai</au><au>Luo, Chunling</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Adaptive differential correspondence imaging based on sorting technique</atitle><jtitle>AIP advances</jtitle><date>2017-04</date><risdate>2017</risdate><volume>7</volume><issue>4</issue><spage>045121</spage><epage>045121-9</epage><pages>045121-045121-9</pages><issn>2158-3226</issn><eissn>2158-3226</eissn><coden>AAIDBI</coden><abstract>We develop an adaptive differential correspondence imaging (CI) method using a sorting technique. Different from the conventional CI schemes, the bucket detector signals (BDS) are first processed by a differential technique, and then sorted in a descending (or ascending) order. Subsequently, according to the front and last several frames of the sorted BDS, the positive and negative subsets (PNS) are created by selecting the relative frames from the reference detector signals. Finally, the object image is recovered from the PNS. Besides, an adaptive method based on two-step iteration is designed to select the optimum number of frames. To verify the proposed method, a single-detector computational ghost imaging (GI) setup is constructed. We experimentally and numerically compare the performance of the proposed method with different GI algorithms. The results show that our method can improve the reconstruction quality and reduce the computation cost by using fewer measurement data.</abstract><cop>Melville</cop><pub>American Institute of Physics</pub><doi>10.1063/1.4982733</doi><tpages>9</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Image reconstruction Iterative methods Sensors Signal processing |
title | Adaptive differential correspondence imaging based on sorting technique |
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