## Compressed sensing in astronomy

Citations: | 23 - 1 self |

### BibTeX

@MISC{Bobin_compressedsensing,

author = {J. Bobin and J-L. Starck and R. Ottensamer},

title = {Compressed sensing in astronomy},

year = {}

}

### OpenURL

### Abstract

Recent advances in signal processing have focused on the use of sparse representations in various applications. A new field of interest based on sparsity has recently emerged: compressed sensing. This theory is a new sampling framework that provides an alternative to the well-known Shannon sampling theory. In this paper we investigate how compressed sensing (CS) can provide new insights into astronomical data compression and more generally how it paves the way for new conceptions in astronomical remote sensing. We first give a brief overview of the compressed sensing theory which provides very simple coding process with low computational cost, thus favoring its use for real-time applications often found on board space mission. We introduce a practical and effective recovery algorithm for decoding compressed data. In astronomy, physical prior information is often crucial for devising effective signal processing methods. We particularly point out that a CS-based compression scheme is flexible enough to account for such information. In this context, compressed sensing is a new framework in which data acquisition and data processing are merged. We show also that CS provides a new fantastic way to handle multiple observations of the same field view, allowing us to recover information at very low signal-to-noise ratio, which is impossible with standard compression methods. This CS data fusion concept could lead to an elegant and effective way to solve the problem ESA is faced with, for the transmission to the earth of the data collected by PACS, one of the instruments on board the Herschel spacecraft which will launched in 2008.

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Citation Context ...imited to: CEA Saclay. Downloaded on December 19, 2008 at 11:26 from IEEE Xplore. Restrictions apply.BOBIN et al.: COMPRESSED SENSING IN ASTRONOMY 719 field, we refer the reader to the review papers =-=[5]-=-, [6]. In this paper, we will assume that the signal belongs to (written as a column vector with entries or samples). We will also assume that is compressible. A. Gist of Compressed Sensing Compressib... |

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Citation Context ...xtended sources. A posteriori, it is not surprising that the best methods which have been proposed in the past for reconstructing such images are based on sparsity. Indeed, the famous CLEAN algorithm =-=[15]-=- and its multiscale version [16], [17] can be seen as matching pursuit algorithms [18], and therefore enforce the sparsity of the solution, in the direct space for CLEAN and in the wavelet space for M... |

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Citation Context ...scale problems. In a different framework, the same kind of optimization problem has been solved using a specific iterative hard-thresholding algorithm coined Morphological Component Analysis (MCA—see =-=[25]-=-) for which the threshold decreases. Inspired by MCA, hard-thresholding is used and the threshold is decreased at each iteration. It starts from and decreases towards . The value of is 0 in the noisel... |

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Citation Context ...first application of compressed sensing then dates back to the 1960s! In the compressed sensing community, the coded mask concept has inspired the design of the celebrated “compressed sensing camera” =-=[13]-=- that provide effective image compression with a single pixel. Simulations involving coded mask for compressive imaging have been made by Willet in [14]. Similarly, (radio-) interferometric data are a... |

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Citation Context ...ot surprising that the best methods which have been proposed in the past for reconstructing such images are based on sparsity. Indeed, the famous CLEAN algorithm [15] and its multiscale version [16], =-=[17]-=- can be seen as matching pursuit algorithms [18], and therefore enforce the sparsity of the solution, in the direct space for CLEAN and in the wavelet space for Multiresolution CLEAN. Recent and CS re... |

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Citation Context ...nomical community is also confronted with a rather desperate need for data compression techniques. Several techniques have in fact been used, or even developed, for astronomical data compression [1], =-=[2]-=-. For some projects, we need to achieve huge compression ratios, which cannot be Manuscript received February 01, 2008; revised August 01, 2008. Current version published December 10, 2008. This work ... |

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