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.
Highlights
Hyperspectral images are comprised of light intensity information from an object or scene, resolved into two spatial dimensions and a spectral dimension, forming a datacube
The multitrack adaptive Compressed sensing (CS) algorithm we present in Section 2.2.3 is an extension of the Adaptive Basis Scan with Wavelet Prediction (ABS-WP) algorithm from [29,39]
We describe the reconstruction algorithm based on block compressed sensing
Summary
Hyperspectral images are comprised of light intensity information from an object or scene, resolved into two spatial dimensions and a spectral (wavelength) dimension, forming a datacube. Variants of HSI, such as Raman chemical imaging [5], where each spatial pixel records a Raman spectrum, are useful as label-free compositional analysis methods for spatially heterogeneous samples Such tools are valuable in biopharmaceutical and cell manufacturing. The biopharmaceutical industry has been trending towards integrated continuous manufacturing [6], where real-time measurements and process control [7] are critical in ensuring and optimizing the quality of the products. This is important due to the stringent regulatory landscape. Optical techniques such as HSI can provide information-rich yet non-contact methods for monitoring and inspection
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