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.
The split Bregman method for L1-regularized problems,
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Empirical comparison on two real chemical-release datasets shows L1 regularization yields better ACE-based chemical detection than total variation at 90% compression.
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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.
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Total variation vs L1 regularization: a comparison of compressive sensing optimization methods for chemical detection
Empirical comparison on two real chemical-release datasets shows L1 regularization yields better ACE-based chemical detection than total variation at 90% compression.