Data-Driven Variable Synthetic Aperture Imaging Based on Semantic Feedback
Synthetic aperture imaging, which has been proved to be an effective approach for occluded object imaging, is one of the challenging problems in the field of computational imaging. Currently most of the related researches focus on fixed synthetic aperture which usually accompanies with mixed observa...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8901214/ |
| _version_ | 1818427896995250176 |
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| author | Congcong Li Jing Li Yanran Dai Tao Yang Yuguang Xie Zhaoyang Lu |
| author_facet | Congcong Li Jing Li Yanran Dai Tao Yang Yuguang Xie Zhaoyang Lu |
| author_sort | Congcong Li |
| collection | DOAJ |
| description | Synthetic aperture imaging, which has been proved to be an effective approach for occluded object imaging, is one of the challenging problems in the field of computational imaging. Currently most of the related researches focus on fixed synthetic aperture which usually accompanies with mixed observation angle and foreground de-focus blur. But the existence of them is frequently a source of perspective effect decrease and occluded object imaging quality degradation. In order to solve this problem, we propose a novel data-driven variable synthetic aperture imaging based on semantic feedback. The semantic content we concerned for better de-occluded imaging is the foreground occlusions rather than the whole scene. Therefore, unlike other methods worked on pixel-level, we start from semantic layer and present a semantic labeling method based on feedback. Semantic labeling map deeply mines visual data in synthetic image and preserves the semantic information of foreground occluder. On the basis of semantic feedback strategy, semantic labeling map will conversely pass to synthetic imaging process. The proposed data-driven variable synthetic aperture imaging contains two levels: one is adaptive changeable imaging aperture driven by synthetic depth and perspective angle, the other is light ray screening driven by visual information in semantic labeling map. On this basis, the hybrid camera view and superimposition of foreground occlusion can be removed. Evaluations on several complex indoor scenes and real outdoor environments demonstrate the superiority and robustness performance of our proposed approach. |
| first_indexed | 2024-12-14T14:53:01Z |
| format | Article |
| id | doaj.art-1b7a5e31c69a47d78850a0427fa59c57 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T14:53:01Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-1b7a5e31c69a47d78850a0427fa59c572022-12-21T22:57:05ZengIEEEIEEE Access2169-35362019-01-01716602116604210.1109/ACCESS.2019.29535608901214Data-Driven Variable Synthetic Aperture Imaging Based on Semantic FeedbackCongcong Li0https://orcid.org/0000-0002-1080-4331Jing Li1https://orcid.org/0000-0002-9043-8633Yanran Dai2Tao Yang3https://orcid.org/0000-0002-5180-2316Yuguang Xie4Zhaoyang Lu5School of Telecommunications Engineering, Xidian University, Xi’an, ChinaSchool of Telecommunications Engineering, Xidian University, Xi’an, ChinaSchool of Telecommunications Engineering, Xidian University, Xi’an, ChinaNational Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, SAIIP, School of Computer Science, Northwestern Polytechnical University, Xi’an, ChinaSchool of Telecommunications Engineering, Xidian University, Xi’an, ChinaSchool of Telecommunications Engineering, Xidian University, Xi’an, ChinaSynthetic aperture imaging, which has been proved to be an effective approach for occluded object imaging, is one of the challenging problems in the field of computational imaging. Currently most of the related researches focus on fixed synthetic aperture which usually accompanies with mixed observation angle and foreground de-focus blur. But the existence of them is frequently a source of perspective effect decrease and occluded object imaging quality degradation. In order to solve this problem, we propose a novel data-driven variable synthetic aperture imaging based on semantic feedback. The semantic content we concerned for better de-occluded imaging is the foreground occlusions rather than the whole scene. Therefore, unlike other methods worked on pixel-level, we start from semantic layer and present a semantic labeling method based on feedback. Semantic labeling map deeply mines visual data in synthetic image and preserves the semantic information of foreground occluder. On the basis of semantic feedback strategy, semantic labeling map will conversely pass to synthetic imaging process. The proposed data-driven variable synthetic aperture imaging contains two levels: one is adaptive changeable imaging aperture driven by synthetic depth and perspective angle, the other is light ray screening driven by visual information in semantic labeling map. On this basis, the hybrid camera view and superimposition of foreground occlusion can be removed. Evaluations on several complex indoor scenes and real outdoor environments demonstrate the superiority and robustness performance of our proposed approach.https://ieeexplore.ieee.org/document/8901214/Synthetic aperture imagingdata-driven variable synthetic aperturesemantic feedback imagingmulti-camera array |
| spellingShingle | Congcong Li Jing Li Yanran Dai Tao Yang Yuguang Xie Zhaoyang Lu Data-Driven Variable Synthetic Aperture Imaging Based on Semantic Feedback IEEE Access Synthetic aperture imaging data-driven variable synthetic aperture semantic feedback imaging multi-camera array |
| title | Data-Driven Variable Synthetic Aperture Imaging Based on Semantic Feedback |
| title_full | Data-Driven Variable Synthetic Aperture Imaging Based on Semantic Feedback |
| title_fullStr | Data-Driven Variable Synthetic Aperture Imaging Based on Semantic Feedback |
| title_full_unstemmed | Data-Driven Variable Synthetic Aperture Imaging Based on Semantic Feedback |
| title_short | Data-Driven Variable Synthetic Aperture Imaging Based on Semantic Feedback |
| title_sort | data driven variable synthetic aperture imaging based on semantic feedback |
| topic | Synthetic aperture imaging data-driven variable synthetic aperture semantic feedback imaging multi-camera array |
| url | https://ieeexplore.ieee.org/document/8901214/ |
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