Compressive sensing reconstruction amplifies chemical signals in hyperspectral cubes, with greater amplification at lower sampling rates, demonstrated on two real chemical simulant datasets using ACE detection.
Multipulse subspace detectors,
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A variance-based ordering of the Walsh-Hadamard sampling basis for compressive sensing outperforms standard orderings, enabling successful chemical detection at 90% compression and over 30% PSNR improvement on depth images.
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More chemical detection through less sampling: amplifying chemical signals in hyperspectral data cubes through compressive sensing
Compressive sensing reconstruction amplifies chemical signals in hyperspectral cubes, with greater amplification at lower sampling rates, demonstrated on two real chemical simulant datasets using ACE detection.
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A data-driven approach to sampling matrix selection for compressive sensing
A variance-based ordering of the Walsh-Hadamard sampling basis for compressive sensing outperforms standard orderings, enabling successful chemical detection at 90% compression and over 30% PSNR improvement on depth images.