We develop, analyize, and numerically validate new recovery algorithms for quantized compressed sensing. Our main focus lies on one-bit quantization. We extend existing theory on one-bit compressed sensing to joint recovery of signal ensembles and introduce new tractable approaches to recover manifold-valued signals from their one-bit measurements. In addition, we develop a highly robust algorithm, which profits from two signal structures at once, to recover matrix valued signals from unquantized compressed sensing measurements.
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We develop, analyize, and numerically validate new recovery algorithms for quantized compressed sensing. Our main focus lies on one-bit quantization. We extend existing theory on one-bit compressed sensing to joint recovery of signal ensembles and introduce new tractable approaches to recover manifold-valued signals from their one-bit measurements. In addition, we develop a highly robust algorithm, which profits from two signal structures at once, to recover matrix valued signals from unquantize...
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