Compact high performance spectrometers using computational imaging

Compressive sensing technology can theoretically be used to develop low cost compact spectrometers with the performance of larger and more expensive systems. Indeed, compressive sensing for spectroscopic systems has been previously demonstrated using coded aperture techniques, wherein a mask is placed between the grating and a charge coupled device (CCD) and multiple measurements are collected with different masks. Although proven effective for some spectroscopic sensing paradigms (e.g. Raman), this approach requires that the signal being measured is static between shots (low noise and minimal signal fluctuation). Many spectroscopic techniques applicable to remote sensing are inherently noisy and thus coded aperture compressed sensing will likely not be effective. This work explores an alternative approach to compressed sensing that allows for reconstruction of a high resolution spectrum in sensing paradigms featuring significant signal fluctuations between measurements. This is accomplished through relatively minor changes to the spectrometer hardware together with custom super-resolution algorithms. Current results indicate that a potential overall reduction in CCD size of up to a factor of 4 can be attained without a loss of resolution. This reduction can result in significant improvements in cost, size, and weight of spectrometers incorporating the technology.