Fast high quality computational ghost imaging based on saliency variable sampling detection
Abstract Fast computational ghost imaging with high quality and ultra-high-definition resolution reconstructed images has important application potential in target tracking, biological imaging and other fields. However, as far as we know, the resolution (pixels) of the reconstructed image is related...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2024-04-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-57866-6 |
| _version_ | 1797219850845683712 |
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| author | Xuan Liu Jun Hu Mingchi Ju Yingzhi Wang Tailin Han Jipeng Huang Cheng Zhou Yongli Zhang Lijun Song |
| author_facet | Xuan Liu Jun Hu Mingchi Ju Yingzhi Wang Tailin Han Jipeng Huang Cheng Zhou Yongli Zhang Lijun Song |
| author_sort | Xuan Liu |
| collection | DOAJ |
| description | Abstract Fast computational ghost imaging with high quality and ultra-high-definition resolution reconstructed images has important application potential in target tracking, biological imaging and other fields. However, as far as we know, the resolution (pixels) of the reconstructed image is related to the number of measurements. And the limited resolution of reconstructed images at low measurement times hinders the application of computational ghost imaging. Therefore, in this work, a new computational ghost imaging method based on saliency variable sampling detection is proposed to achieve high-quality imaging at low measurement times. This method physically variable samples the salient features and realizes compressed detection of computational ghost imaging based on the salient periodic features of the bucket detection signal. Numerical simulation and experimental results show that the reconstructed image quality of our method is similar to the compressed sensing method at low measurement times. Even at 500 (sampling rate $$0.76\%$$ 0.76 % ) measurement times, the reconstructed image of the method still has the target features. Moreover, the $$2160\times 4096$$ 2160 × 4096 (4K) pixels ultra-high-definition resolution reconstructed images can be obtained at only a sampling rate of $$0.11\%$$ 0.11 % . This method has great potential value in real-time detection and tracking, biological imaging and other fields. |
| first_indexed | 2024-04-24T12:40:12Z |
| format | Article |
| id | doaj.art-2a69bdcf679f46a69e829af30dc74153 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-24T12:40:12Z |
| publishDate | 2024-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-2a69bdcf679f46a69e829af30dc741532024-04-07T11:16:51ZengNature PortfolioScientific Reports2045-23222024-04-0114111510.1038/s41598-024-57866-6Fast high quality computational ghost imaging based on saliency variable sampling detectionXuan Liu0Jun Hu1Mingchi Ju2Yingzhi Wang3Tailin Han4Jipeng Huang5Cheng Zhou6Yongli Zhang7Lijun Song8College of Electronic and Information Engineering, Changchun University of Science and TechnologyCollege of Electronic and Information Engineering, Changchun University of Science and TechnologyCollege of Electronic and Information Engineering, Changchun University of Science and TechnologyCollege of Electronic and Information Engineering, Changchun University of Science and TechnologyCollege of Electronic and Information Engineering, Changchun University of Science and TechnologyCollege of physics, Northeast Normal UniversityCollege of physics, Northeast Normal UniversityAcademy of Agricultural Planning and Engineering, Ministry of Agriculture and Rural AffairsChangchun Institute of TechnologyAbstract Fast computational ghost imaging with high quality and ultra-high-definition resolution reconstructed images has important application potential in target tracking, biological imaging and other fields. However, as far as we know, the resolution (pixels) of the reconstructed image is related to the number of measurements. And the limited resolution of reconstructed images at low measurement times hinders the application of computational ghost imaging. Therefore, in this work, a new computational ghost imaging method based on saliency variable sampling detection is proposed to achieve high-quality imaging at low measurement times. This method physically variable samples the salient features and realizes compressed detection of computational ghost imaging based on the salient periodic features of the bucket detection signal. Numerical simulation and experimental results show that the reconstructed image quality of our method is similar to the compressed sensing method at low measurement times. Even at 500 (sampling rate $$0.76\%$$ 0.76 % ) measurement times, the reconstructed image of the method still has the target features. Moreover, the $$2160\times 4096$$ 2160 × 4096 (4K) pixels ultra-high-definition resolution reconstructed images can be obtained at only a sampling rate of $$0.11\%$$ 0.11 % . This method has great potential value in real-time detection and tracking, biological imaging and other fields.https://doi.org/10.1038/s41598-024-57866-6 |
| spellingShingle | Xuan Liu Jun Hu Mingchi Ju Yingzhi Wang Tailin Han Jipeng Huang Cheng Zhou Yongli Zhang Lijun Song Fast high quality computational ghost imaging based on saliency variable sampling detection Scientific Reports |
| title | Fast high quality computational ghost imaging based on saliency variable sampling detection |
| title_full | Fast high quality computational ghost imaging based on saliency variable sampling detection |
| title_fullStr | Fast high quality computational ghost imaging based on saliency variable sampling detection |
| title_full_unstemmed | Fast high quality computational ghost imaging based on saliency variable sampling detection |
| title_short | Fast high quality computational ghost imaging based on saliency variable sampling detection |
| title_sort | fast high quality computational ghost imaging based on saliency variable sampling detection |
| url | https://doi.org/10.1038/s41598-024-57866-6 |
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