Fast reconstruction of hyperspectral images from coded acquisitions using a separability assumption
Résumé
We present a fast reconstruction algorithm for hyperspectral images, utilizing a small amount of data without the need for any training. The method is implemented with a dual disperser hyperspectral imager, and makes use of spatial-spectral correlations by a so-called separability assumption which assumes that the image is made of regions of homogenous spectra. The reconstruction algorithm is simple and ready-to-use, and does not require any prior knowledge of the scene. A simple proof-of-principle experiment is performed, demonstrating that only a small number of acquisitions are required, and the resulting compressed data-cube is reconstructed near instantaneously.
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