Cost-efficient Active Illumination Camera For Hyper-spectral Reconstruction
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | |
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| _version_ | 1866910505659006976 |
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| author | Zhang, Yuxuan Sazzad, T. M. Song, Yangyang Chang, Spencer J. Chowdhry, Ritesh Mejia, Tomas Hampton, Anna Kucharski, Shelby Gerber, Stefan Tillman, Barry Resende, Marcio F. R. Hammond, William M. Wilson, Chris H. Zare, Alina Koppal, Sanjeev J. |
| author_facet | Zhang, Yuxuan Sazzad, T. M. Song, Yangyang Chang, Spencer J. Chowdhry, Ritesh Mejia, Tomas Hampton, Anna Kucharski, Shelby Gerber, Stefan Tillman, Barry Resende, Marcio F. R. Hammond, William M. Wilson, Chris H. Zare, Alina Koppal, Sanjeev J. |
| contents | Hyper-spectral imaging has recently gained increasing attention for use in different applications, including agricultural investigation, ground tracking, remote sensing and many other. However, the high cost, large physical size and complicated operation process stop hyperspectral cameras from being employed for various applications and research fields. In this paper, we introduce a cost-efficient, compact and easy to use active illumination camera that may benefit many applications. We developed a fully functional prototype of such camera. With the hope of helping with agricultural research, we tested our camera for plant root imaging. In addition, a U-Net model for spectral reconstruction was trained by using a reference hyperspectral camera's data as ground truth and our camera's data as input. We demonstrated our camera's ability to obtain additional information over a typical RGB camera. In addition, the ability to reconstruct hyperspectral data from multi-spectral input makes our device compatible to models and algorithms developed for hyperspectral applications with no modifications required. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_19560 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Cost-efficient Active Illumination Camera For Hyper-spectral Reconstruction Zhang, Yuxuan Sazzad, T. M. Song, Yangyang Chang, Spencer J. Chowdhry, Ritesh Mejia, Tomas Hampton, Anna Kucharski, Shelby Gerber, Stefan Tillman, Barry Resende, Marcio F. R. Hammond, William M. Wilson, Chris H. Zare, Alina Koppal, Sanjeev J. Computer Vision and Pattern Recognition Machine Learning Image and Video Processing Hyper-spectral imaging has recently gained increasing attention for use in different applications, including agricultural investigation, ground tracking, remote sensing and many other. However, the high cost, large physical size and complicated operation process stop hyperspectral cameras from being employed for various applications and research fields. In this paper, we introduce a cost-efficient, compact and easy to use active illumination camera that may benefit many applications. We developed a fully functional prototype of such camera. With the hope of helping with agricultural research, we tested our camera for plant root imaging. In addition, a U-Net model for spectral reconstruction was trained by using a reference hyperspectral camera's data as ground truth and our camera's data as input. We demonstrated our camera's ability to obtain additional information over a typical RGB camera. In addition, the ability to reconstruct hyperspectral data from multi-spectral input makes our device compatible to models and algorithms developed for hyperspectral applications with no modifications required. |
| title | Cost-efficient Active Illumination Camera For Hyper-spectral Reconstruction |
| topic | Computer Vision and Pattern Recognition Machine Learning Image and Video Processing |
| url | https://arxiv.org/abs/2406.19560 |