Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/153897
Title: Multitrack compressed sensing for faster hyperspectral imaging
Authors: Kubal, Sharvaj
Lee, Elizabeth
Tay, Chor Yong
Yong, Derrick
Keywords: Engineering::Materials
Science::Biological sciences
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

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