@inproceedings{faa57105ab7e4949835bd5b746a280c3,
title = "Designing constrained projections for compressed sensing: Mean errors and anomalies with coherence",
abstract = "Most existing work in designing sensing matrices for compressive recovery is based on optimizing some quality factor, such as mutual coherence, average coherence or the restricted isometry constant (RIC), of the sensing matrix. In this paper, we report anomalous results that show that such a design is not always guaranteed to improve reconstruction results. We also present a design method based on the minimum mean squared error (MMSE) criterion, imposing priors on signal and noise for natural images, and show that it yields results superior to results from coherence-based methods while taking into account physical constraints on the sensing matrix.",
keywords = "Average coherence, Bayesian estimation, Compressed sensing, Projection design, Structured sparsity",
author = "Dhruv Shah and Alankar Kotwal and Ajit Rajwade",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 ; Conference date: 26-11-2018 Through 29-11-2018",
year = "2018",
month = jul,
day = "2",
doi = "10.1109/GlobalSIP.2018.8646698",
language = "English (US)",
series = "2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "321--325",
booktitle = "2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings",
}