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**Compressive sampling.**
*(English)*
Zbl 1130.94013

Sanz-Solé, Marta (ed.) et al., Proceedings of the international congress of mathematicians (ICM), Madrid, Spain, August 22–30, 2006. Volume III: Invited lectures. Zürich: European Mathematical Society (EMS) (ISBN 978-3-03719-022-7/hbk). 1433-1452 (2006).

Summary: Conventional wisdom and common practice in acquisition and reconstruction of images from frequency data follow the basic principle of the Nyquist density sampling theory. This principle states that to reconstruct an image, the number of Fourier samples we need to acquire must match the desired resolution of the image, i.e., the number of pixels in the image. This paper surveys an emerging theory which goes by the name of “compressive sampling” or “compressed sensing”, and which says that this conventional wisdom is inaccurate. Perhaps surprisingly, it is possible to reconstruct images or signals of scientific interest accurately and sometimes even exactly from a number of samples which is far smaller than the desired resolution of the image/signal, e.g. the number of pixels in the image.

For the entire collection see [Zbl 1095.00006].

For the entire collection see [Zbl 1095.00006].

### MSC:

94A20 | Sampling theory in information and communication theory |

62D05 | Sampling theory, sample surveys |

62H35 | Image analysis in multivariate analysis |

68P30 | Coding and information theory (compaction, compression, models of communication, encoding schemes, etc.) (aspects in computer science) |

94A08 | Image processing (compression, reconstruction, etc.) in information and communication theory |

68U10 | Computing methodologies for image processing |