Compressive hyperspectral imaging in the molecular fingerprint band

Abstract

Spectrally-resolved imaging provides a spectrum for each pixel of an image that, in the mid-infrared, can enable its chemical composition to be mapped by exploiting the correlation between spectroscopic features and specific molecular groups. The compatibility of Fourier-transform interferometry with full-field imaging makes it the spectroscopic method of choice, but Nyquist-limited fringe sampling restricts the increments of the interferometer arm length to no more than a few microns, making the acquisition time-consuming. Here, we demonstrate a compressive hyperspectral imaging strategy that combines non-uniform sampling and a smoothness-promoting prior to acquire data at 15% of the Nyquist rate, providing a significant acquisition-rate improvement over state-of-the-art techniques. By illuminating test objects with a sequence of suitably designed light spectra, we demonstrate compressive hyperspectral imaging across the 700–1400 cm-1 region in transmission mode. A post-processing analysis of the resulting hyperspectral images shows the potential of the method for efficient non-destructive classification of different materials on painted cultural heritage.

Roadmap on spatiotemporal light fields

“…Reducing the number of acquisitions in a multi-shot device can also be achieved by compressed sensing, where choosing random sampling positions can produce similarly resolved spectral images with fewer total measurements. This has been applied to hyperspectral imaging that utilized Fouriertransform interferometry [139], where the number of shots was reduced to considerably sub-Nyquist acquisition rates (down to 15% of the Nyquist limit) by combining bandpass and nonuniform compressive sampling.…”

High-speed hyperspectral imaging enabled by compressed sensing in time domain

“…1(b)], which provides a technique to recover a complete data set from fewer measurements than what is required by the Nyquist–Shannon sampling theorem based on two assumptions: (i) the signal from the target object is sparse in a suitable domain and (ii) the measurement process is incoherent; the sampling intervals along the τ axis are randomized in the case of Fourier-transformation-based measurements like time-domain HSI. Although CS has been demonstrated for time-domain HSI, the methods implemented in the previous demonstrations are not applicable to high-speed hyperspectral image acquisition because the requisite sampling randomization was implemented during postmeasurement processing 16 or was paired with a lower camera frame rate 17 …”

“…Although CS has been demonstrated for time-domain HSI, the methods implemented in the previous demonstrations are not applicable to high-speed hyperspectral image acquisition because the requisite sampling randomization was implemented during postmeasurement processing 16 or was paired with a lower camera frame rate. 17 In this paper, we report a CS-powered method to achieve high-speed time-domain HSI. Specifically, we used broadband (200 to 1600 cm −1 ) Fourier-transform coherent anti-Stokes Raman scattering (FT-CARS), a type of nonlinear Raman process, 13,18,19 as a platform to evaluate this method.…”