Sparse Measurement Matrices for Compressed-Sensing Recovery by Bayesian Approximate Message Passing | VDE Conference Publication | IEEE Xplore

Sparse Measurement Matrices for Compressed-Sensing Recovery by Bayesian Approximate Message Passing

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Abstract:

Sparse measurement matrices with very few randomly selected +1/-1 non-zero elements are designed for use with Bayesian Approximate Message Passing as a compressed sensing...Show More

Abstract:

Sparse measurement matrices with very few randomly selected +1/-1 non-zero elements are designed for use with Bayesian Approximate Message Passing as a compressed sensing recovery algorithm. Simulations show that such sparse matrices, which allow for large savings in storage and computation time, can achieve a recovery performance that is as good as the benchmark given by random Gaussian matrices.
Date of Conference: 18-20 February 2020
Date Added to IEEE Xplore: 20 May 2020
Print ISBN:978-3-8007-5200-3
Conference Location: Hamburg, Germany