Abstract
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.
Original language | English GB |
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DOIs | |
State | Published - 1 Jan 2019 |
Event | Big Data: Learning, Analytics, and Applications 2019 - Baltimore, United States Duration: 17 Apr 2019 → 18 Apr 2019 |
Conference
Conference | Big Data: Learning, Analytics, and Applications 2019 |
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Country/Territory | United States |
City | Baltimore |
Period | 17/04/19 → 18/04/19 |
Keywords
- Compressive sensing
- Compressive spectroscopy
- Deep Neural Networks
- Hyperspectral imaging
ASJC Scopus subject areas
- Electronic, Optical and Magnetic Materials
- Condensed Matter Physics
- Computer Science Applications
- Applied Mathematics
- Electrical and Electronic Engineering