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|Title:||Multitrack compressed sensing for faster hyperspectral imaging||Authors:||Kubal, Sharvaj
Tay, Chor Yong
|Issue Date:||2021||Source:||Kubal, S., Lee, E., Tay, C. Y. & Yong, D. (2021). Multitrack compressed sensing for faster hyperspectral imaging. Sensors, 21(15), 5034-. https://dx.doi.org/10.3390/s21155034||Journal:||Sensors||Abstract:||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.||URI:||https://hdl.handle.net/10356/153897||ISSN:||1424-8220||DOI:||10.3390/s21155034||Rights:||© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||MSE Journal Articles|
Updated on Dec 1, 2022
Updated on Dec 1, 2022
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