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|a 10.1109/TIP.2011.2163520
|2 doi
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|a eng
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|a Wu, Xiaolin
|e verfasserin
|4 aut
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|a Model-assisted adaptive recovery of compressed sensing with imaging applications
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|c 2012
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|a Text
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Completed 16.05.2012
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|a Date Revised 20.01.2012
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a © 2011 IEEE
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|a In compressive sensing (CS), a challenge is to find a space in which the signal is sparse and, hence, faithfully recoverable. Since many natural signals such as images have locally varying statistics, the sparse space varies in time/spatial domain. As such, CS recovery should be conducted in locally adaptive signal-dependent spaces to counter the fact that the CS measurements are global and irrespective of signal structures. On the contrary, existing CS reconstruction methods use a fixed set of bases (e.g., wavelets, DCT, and gradient spaces) for the entirety of a signal. To rectify this problem, we propose a new framework for model-guided adaptive recovery of compressive sensing (MARX) and show how a 2-D piecewise autoregressive model can be integrated into the MARX framework to make CS recovery adaptive to spatially varying second order statistics of an image. In addition, MARX offers a mechanism of characterizing and exploiting structured sparsities of natural images, greatly restricting the CS solution space. Simulation results over a wide range of natural images show that the proposed MARX technique can improve the reconstruction quality of existing CS methods by 2-7 dB
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|a Journal Article
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|a Research Support, Non-U.S. Gov't
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|a Dong, Weisheng
|e verfasserin
|4 aut
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|a Zhang, Xiangjun
|e verfasserin
|4 aut
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| 700 |
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|a Shi, Guangming
|e verfasserin
|4 aut
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 21(2012), 2 vom: 05. Feb., Seite 451-8
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|x 1941-0042
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|g year:2012
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|u http://dx.doi.org/10.1109/TIP.2011.2163520
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