Model-Based Compressive Sensing for Signal Ensembles
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Compressed sensing is a new trend in signal processing for efficient sampling and signal acquisition. The idea is that most real-world signals have a sparse representation in an appropriate basis and this can be exploited to capture the sparse signal by ta ...
Over the past decade researches in applied mathematics, signal processing and communications have introduced compressive sampling (CS) as an alternative to the Shannon sampling theorem. The two key observations making CS theory widely applicable to numerou ...
Compressive sensing (CS) is an alternative to Shannon/Nyquist sampling for acquisition of sparse or compressible signals that can be well approximated by just K < N elements from an N-dimensional basis. Instead of taking periodic samples, we measure inner ...
Institute of Electrical and Electronics Engineers2010
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Compressive sensing (CS) is an alternative to Shannon/Nyquist sampling for acquisition of sparse or compressible signals; instead of taking periodic samples, we measure inner products with All < N random vectors and then recover the signal via a sparsity-s ...
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