DocumentCode :
2803579
Title :
Adaptive structured recovery of compressive sensing via piecewise autoregressive modeling
Author :
Wu, Xiaolin ; Zhang, Xiangjun
Author_Institution :
Dept. of Electr. & Comput. Eng., McMaster Univ., Hamilton, ON, Canada
fYear :
2010
fDate :
14-19 March 2010
Firstpage :
3906
Lastpage :
3909
Abstract :
In compressive sensing (CS) a challenge is to find a space in which the signal is sparse and hence recoverable faithfully and efficiently. Given the nonstationarity of many natural signals such as images, 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 most CS methods seek for 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 piecewise autoregressive model can be integrated into the MARX framework to adapt to changing second order statistics of a signal in CS recovery. In addition, MARX offers a powerful mechanism of characterizing and exploiting structured sparsities of a signal, greatly restricting the CS solution space. A case study on CS-acquired images shows that the proposed MARX technique can increase the reconstruction quality by up to 8 dB over existing methods.
Keywords :
autoregressive processes; higher order statistics; signal reconstruction; CS measurements; CS recovery; CS solution space; CS-acquired images; MARX framework; adaptive structured recovery; compressive sensing; locally adaptive signal-dependent spaces; model-guided adaptive recovery; natural signals; piecewise autoregressive modeling; reconstruction quality; second order statistics; signal structures; sparse space; spatial domain; structured sparsities; time domain; Adaptive signal processing; Autoregressive processes; Counting circuits; Discrete cosine transforms; Extraterrestrial measurements; Image coding; Image reconstruction; Inverse problems; Matching pursuit algorithms; Statistics; adaptive modeling; autoregressive process; compressive sensing; inverse problem;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics Speech and Signal Processing (ICASSP), 2010 IEEE International Conference on
Conference_Location :
Dallas, TX
ISSN :
1520-6149
Print_ISBN :
978-1-4244-4295-9
Electronic_ISBN :
1520-6149
Type :
conf
DOI :
10.1109/ICASSP.2010.5495811
Filename :
5495811
Link To Document :
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