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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| Summary: | 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. |
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| ISSN: | 2096-4579 |