In the recent years, we have developed several architectures for compressive hyperspectral (HS) imagers. The compressive sensing (CS) design has allowed the reduction of the enormous acquisition effort associated with the huge dimensionality of the HS data. Unfortunately, the reduced sensing effort offered by the CS approach comes on the account of increased post-sensing computational burden. Conventional CS reconstruction involves algorithms that solve a ℓ₁ minimization problem. Those algorithms are iterative and typically very computationally heavy. The computation burden is even more prominent when reconstructing 3D HS data, where each spectral image may have Gigavoxel size. Motivated by this, we have investigated replacing the CS iterative reconstruction step with an appropriate Deep Neural Network.

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Yaron Heiser, Yaniv Oiknine, and Adrian Stern, "Compressive hyperspectral image reconstruction with deep neural networks," Proc. SPIE 10989, Big Data: Learning, Analytics, and Applications, 109890M (Presented at SPIE Defense + Commercial Sensing: April 18, 2019; Published: 13 May 2019); https://doi.org/10.1117/12.2522122.