Abstract
Compressive sensing is a recent type of sampling theory, which predicts that sparse signals and images can be reconstructed from what was previously believed to be incomplete information. As a main feature, efficient algorithms such as `1-minimization can be used for recovery. The theory has many potential applications in signal processing and imaging. This chapter gives an introduction and overview on both theoretical and numerical aspects of compressive sensing.
Cite
CITATION STYLE
Fornasier, M., & Rauhut, H. (2015). Compressive sensing. In Handbook of Mathematical Methods in Imaging: Volume 1, Second Edition (pp. 205–256). Springer New York. https://doi.org/10.1007/978-1-4939-0790-8_6
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