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More chemical detection through less sampling: amplifying chemical signals in hyperspectral data cubes through compressive sensing
In this paper we explore a phenomenon in which bandwise CS sampling of a hyperspectral data cube followed by reconstruction can actually result in amplification of chemical signals contained in the cube.
A data-driven approach to sampling matrix selection for compressive sensing
We propose a method for defining an order for a sampling basis that is optimal with respect to capturing variance in data, thus allowing for meaningful sensing at any desired level of compression.
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