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In this work, we show that reconstructing a sparse signal from quantized compressive measurement can be achieved in an unified formalism whatever the (scalar) quantization resolution, i.e., from 1-bit to high resolution assumption.
Convex analysis
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“Dequantizing Compressed Sensing: When Oversampling and Non-Gaussian Constraints Combine.,”
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“Accelerated iterative hard thresholding,”
T. Blumensath, · 2012
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“Sobolev duals for random frames and Σ \Sigma Δ \Delta quantization of compressed sensing measurements,”
C. Sinan Güntürk, M Lammers, A. M. Powell, R. Saab, and Ö. Yılmaz, · 2013
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“Robust 1-bit compressive sensing via binary stable embeddings of sparse vectors,”
L. Jacques, J.N. Laska, P.T. Boufounos, and R.G. Baraniuk, · 2013
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Y. Plan and R. Vershynin, · 2013
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“Robust 1-bit compressed sensing and sparse logistic regression: A convex programming approach,”
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“Stabilizing nonuniformly quantized compressed sensing with scalar companders,”
L. Jacques, D. K. Hammond, and M. J. Fadili, · 2012
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Y. Plan and R. Vershynin, · 2013
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“Robust 1-bit Compressive Sensing via Gradient Support Pursuit,”
S. Bahmani, P. T. Boufounos and B. Raj, · 2013
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