Data-driven polarimetric approaches fuel computational imaging expansion
Incorporating polarization in computer vision tasks provides new solutions to high-level analytics, in particular when coupled with machine learning frameworks such as convolutional neural networks (CNN). A recent review in Opto-Electronic Science reports on the developments in data-driven polarimet...
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| Format: | Article |
| Language: | English |
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Institue of Optics and Electronics, Chinese Academy of Sciences
2024-09-01
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| Series: | Opto-Electronic Advances |
| Online Access: | https://www.oejournal.org/article/doi/10.29026/oea.2024.240158 |
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| _version_ | 1850265578048061440 |
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| author | Sylvain Gigan |
| author_facet | Sylvain Gigan |
| author_sort | Sylvain Gigan |
| collection | DOAJ |
| description | Incorporating polarization in computer vision tasks provides new solutions to high-level analytics, in particular when coupled with machine learning frameworks such as convolutional neural networks (CNN). A recent review in Opto-Electronic Science reports on the developments in data-driven polarimetric imaging, including polarimetric descattering, 3D imaging, reflection removal, target detection and biomedical imaging. The review carefully analyzes these new trends with their advantages and disadvantages, and provides a general insight for future research and development. |
| format | Article |
| id | doaj-art-eb64aa3d9c12461987fd60343e75d2d0 |
| institution | OA Journals |
| issn | 2096-4579 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | Institue of Optics and Electronics, Chinese Academy of Sciences |
| record_format | Article |
| series | Opto-Electronic Advances |
| spelling | doaj-art-eb64aa3d9c12461987fd60343e75d2d02025-08-20T01:54:23ZengInstitue of Optics and Electronics, Chinese Academy of SciencesOpto-Electronic Advances2096-45792024-09-01791310.29026/oea.2024.240158OEA-2024-0158SylvainGiganData-driven polarimetric approaches fuel computational imaging expansionSylvain Gigan0Laboratoire Kastler Brossel, École Normale Supérieure/PSL Research University, Paris 75005, FranceIncorporating polarization in computer vision tasks provides new solutions to high-level analytics, in particular when coupled with machine learning frameworks such as convolutional neural networks (CNN). A recent review in Opto-Electronic Science reports on the developments in data-driven polarimetric imaging, including polarimetric descattering, 3D imaging, reflection removal, target detection and biomedical imaging. The review carefully analyzes these new trends with their advantages and disadvantages, and provides a general insight for future research and development.https://www.oejournal.org/article/doi/10.29026/oea.2024.240158 |
| spellingShingle | Sylvain Gigan Data-driven polarimetric approaches fuel computational imaging expansion Opto-Electronic Advances |
| title | Data-driven polarimetric approaches fuel computational imaging expansion |
| title_full | Data-driven polarimetric approaches fuel computational imaging expansion |
| title_fullStr | Data-driven polarimetric approaches fuel computational imaging expansion |
| title_full_unstemmed | Data-driven polarimetric approaches fuel computational imaging expansion |
| title_short | Data-driven polarimetric approaches fuel computational imaging expansion |
| title_sort | data driven polarimetric approaches fuel computational imaging expansion |
| url | https://www.oejournal.org/article/doi/10.29026/oea.2024.240158 |
| work_keys_str_mv | AT sylvaingigan datadrivenpolarimetricapproachesfuelcomputationalimagingexpansion |