Multitrack Compressed Sensing for Faster Hyperspectral Imaging
Hyperspectral imaging (HSI) provides additional information compared to regular color imaging, making it valuable in areas such as biomedicine, materials inspection and food safety. However, HSI is challenging because of the large amount of data and long measurement times involved. Compressed sensin...
| Main Authors: | , , , |
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| Format: | Article |
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Multidisciplinary Digital Publishing Institute
2021
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| Online Access: | https://hdl.handle.net/1721.1/133208 |
| _version_ | 1826208981953019904 |
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| author | Kubal, Sharvaj Lee, Elizabeth Tay, Chor Yong Yong, Derrick |
| author_facet | Kubal, Sharvaj Lee, Elizabeth Tay, Chor Yong Yong, Derrick |
| author_sort | Kubal, Sharvaj |
| collection | MIT |
| description | Hyperspectral imaging (HSI) provides additional information compared to regular color imaging, making it valuable in areas such as biomedicine, materials inspection and food safety. However, HSI is challenging because of the large amount of data and long measurement times involved. Compressed sensing (CS) approaches to HSI address this, albeit subject to tradeoffs between image reconstruction accuracy, time and generalizability to different types of scenes. Here, we develop improved CS approaches for HSI, based on parallelized multitrack acquisition of multiple spectra per shot. The multitrack architecture can be paired up with either of the two compatible CS algorithms developed here: (1) a sparse recovery algorithm based on block compressed sensing and (2) an adaptive CS algorithm based on sampling in the wavelet domain. As a result, the measurement speed can be drastically increased while maintaining reconstruction speed and accuracy. The methods were validated computationally both in noiseless as well as noisy simulated measurements. Multitrack adaptive CS has a ∼10 times shorter measurement plus reconstruction time as compared to full sampling HSI without compromising reconstruction accuracy across the sample images tested. Multitrack non-adaptive CS (sparse recovery) is most robust against Poisson noise at the expense of longer reconstruction times. |
| first_indexed | 2024-09-23T14:15:29Z |
| format | Article |
| id | mit-1721.1/133208 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T14:15:29Z |
| publishDate | 2021 |
| publisher | Multidisciplinary Digital Publishing Institute |
| record_format | dspace |
| spelling | mit-1721.1/1332082021-11-01T14:36:56Z Multitrack Compressed Sensing for Faster Hyperspectral Imaging Kubal, Sharvaj Lee, Elizabeth Tay, Chor Yong Yong, Derrick Hyperspectral imaging (HSI) provides additional information compared to regular color imaging, making it valuable in areas such as biomedicine, materials inspection and food safety. However, HSI is challenging because of the large amount of data and long measurement times involved. Compressed sensing (CS) approaches to HSI address this, albeit subject to tradeoffs between image reconstruction accuracy, time and generalizability to different types of scenes. Here, we develop improved CS approaches for HSI, based on parallelized multitrack acquisition of multiple spectra per shot. The multitrack architecture can be paired up with either of the two compatible CS algorithms developed here: (1) a sparse recovery algorithm based on block compressed sensing and (2) an adaptive CS algorithm based on sampling in the wavelet domain. As a result, the measurement speed can be drastically increased while maintaining reconstruction speed and accuracy. The methods were validated computationally both in noiseless as well as noisy simulated measurements. Multitrack adaptive CS has a ∼10 times shorter measurement plus reconstruction time as compared to full sampling HSI without compromising reconstruction accuracy across the sample images tested. Multitrack non-adaptive CS (sparse recovery) is most robust against Poisson noise at the expense of longer reconstruction times. 2021-10-27T18:40:36Z 2021-10-27T18:40:36Z 2021-07-24 2021-08-06T15:19:17Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/133208 Sensors 21 (15): 5034 (2021) PUBLISHER_CC http://dx.doi.org/10.3390/s21155034 Creative Commons Attribution https://creativecommons.org/licenses/by/4.0/ application/pdf Multidisciplinary Digital Publishing Institute Multidisciplinary Digital Publishing Institute |
| spellingShingle | Kubal, Sharvaj Lee, Elizabeth Tay, Chor Yong Yong, Derrick Multitrack Compressed Sensing for Faster Hyperspectral Imaging |
| title | Multitrack Compressed Sensing for Faster Hyperspectral Imaging |
| title_full | Multitrack Compressed Sensing for Faster Hyperspectral Imaging |
| title_fullStr | Multitrack Compressed Sensing for Faster Hyperspectral Imaging |
| title_full_unstemmed | Multitrack Compressed Sensing for Faster Hyperspectral Imaging |
| title_short | Multitrack Compressed Sensing for Faster Hyperspectral Imaging |
| title_sort | multitrack compressed sensing for faster hyperspectral imaging |
| url | https://hdl.handle.net/1721.1/133208 |
| work_keys_str_mv | AT kubalsharvaj multitrackcompressedsensingforfasterhyperspectralimaging AT leeelizabeth multitrackcompressedsensingforfasterhyperspectralimaging AT taychoryong multitrackcompressedsensingforfasterhyperspectralimaging AT yongderrick multitrackcompressedsensingforfasterhyperspectralimaging |