High-throughput hyperspectral imaging with tomographic reconstruction for weak signal sources

This paper presents a novel technique for wide field hyperspectral imaging using a tomographic approach which yields superior throughput over conventional schemes, an attribute that is highly desirable in spectral imaging of low signal-to-noise ratio sources. This technique is capable of simultaneous multi-object spectroscopy on point sources and imaging spectroscopy of faint diffuse emissions, and consequently has applications in astrophysical and space remote imaging at ultraviolet (UV) and optical wavelengths. Unlike conventional hyperspectral imaging techniques which raster the scene through a slit spectrograph over some time, this method encodes three dimensions of data (two spatial and one spectral) into a two-dimensional signal, using an optical system where photons from the entire scene are collected at all times. Inversion of this data back into the three-dimensional space is accomplished by formulating a tomographic reconstruction problem. Intrinsic presence of noise, however, poses difficulties on conventional inversion methods such as the filtered back-projection (FBP) or the minimum norm least squares methods such that they often produce unacceptable results. This limitation is overcome by the incorporation of regularization schemes, minimizing appropriate l₁ and l₂ norm functionals. Through simulations, the performance of this technique is demonstrated quantitatively in comparison with conventional spectral imaging techniques