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...

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Vydané v:Sensors (Basel, Switzerland) Ročník 21; číslo 15; s. 5034
Hlavní autori: Kubal, Sharvaj, Lee, Elizabeth, Tay, Chor Yong, Yong, Derrick
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Basel MDPI AG 24.07.2021
MDPI
Predmet:
adaptive imaging
Algorithms
Arrays
compressed sensing
hyperspectral imaging
Light
Manufacturing
Neural networks
Process controls
wavelets
ISSN:1424-8220, 1424-8220
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Popis
Shrnutí: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.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s21155034