A compressive sensing reconstruction algorithm for trinary and binary sparse signals using pre-mapping

Xinyu Zhang; Zhuoyuan Chen; Jiangtao Wen; Jianwei Ma; Yuxing Han; John Villasenor

doi:10.1109/DCC.2011.27

A compressive sensing reconstruction algorithm for trinary and binary sparse signals using pre-mapping

Xinyu Zhang^*
, Zhuoyuan Chen
, Jiangtao Wen
, Jianwei Ma
, Yuxing Han
, John Villasenor

^*Corresponding author for this work

Tsinghua University
Florida State University
University of California at Los Angeles

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

In this paper, we first analyze impact of the distribution of sparse signals on reconstruction quality in compressive sensing through experimental results and heuristic analysis. We suggest that trinary/binary sparse signals are one of the most difficult signals to reconstruct in terms of error bounds. We then show that by incorporating linear or non-linear mapping prior to sensing, significant improvement in the recovery performance can be achieved.

Original language	English
Title of host publication	Proceedings - DCC 2011
Subtitle of host publication	2011 Data Compression Conference
Pages	203-212
Number of pages	10
DOIs	https://doi.org/10.1109/DCC.2011.27
State	Published - 2011
Externally published	Yes
Event	2011 Data Compression Conference, DCC 2011 - Snowbird, UT, United States Duration: 29 Mar 2011 → 31 Mar 2011

Publication series

Name	Data Compression Conference Proceedings
ISSN (Print)	1068-0314

Conference

Conference	2011 Data Compression Conference, DCC 2011
Country/Territory	United States
City	Snowbird, UT
Period	29/03/11 → 31/03/11

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10.1109/DCC.2011.27

Cite this

@inproceedings{72d4d990bb534e229c0584e1649b7384,

title = "A compressive sensing reconstruction algorithm for trinary and binary sparse signals using pre-mapping",

abstract = "In this paper, we first analyze impact of the distribution of sparse signals on reconstruction quality in compressive sensing through experimental results and heuristic analysis. We suggest that trinary/binary sparse signals are one of the most difficult signals to reconstruct in terms of error bounds. We then show that by incorporating linear or non-linear mapping prior to sensing, significant improvement in the recovery performance can be achieved.",

author = "Xinyu Zhang and Zhuoyuan Chen and Jiangtao Wen and Jianwei Ma and Yuxing Han and John Villasenor",

year = "2011",

doi = "10.1109/DCC.2011.27",

language = "英语",

isbn = "9780769543529",

series = "Data Compression Conference Proceedings",

pages = "203--212",

booktitle = "Proceedings - DCC 2011",

note = "2011 Data Compression Conference, DCC 2011 ; Conference date: 29-03-2011 Through 31-03-2011",

}

Zhang, X, Chen, Z, Wen, J, Ma, J, Han, Y & Villasenor, J 2011, A compressive sensing reconstruction algorithm for trinary and binary sparse signals using pre-mapping. in Proceedings - DCC 2011: 2011 Data Compression Conference., 5749478, Data Compression Conference Proceedings, pp. 203-212, 2011 Data Compression Conference, DCC 2011, Snowbird, UT, United States, 29/03/11. https://doi.org/10.1109/DCC.2011.27

A compressive sensing reconstruction algorithm for trinary and binary sparse signals using pre-mapping. / Zhang, Xinyu; Chen, Zhuoyuan; Wen, Jiangtao et al.
Proceedings - DCC 2011: 2011 Data Compression Conference. 2011. p. 203-212 5749478 (Data Compression Conference Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

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AU - Han, Yuxing

AU - Villasenor, John

PY - 2011

Y1 - 2011

N2 - In this paper, we first analyze impact of the distribution of sparse signals on reconstruction quality in compressive sensing through experimental results and heuristic analysis. We suggest that trinary/binary sparse signals are one of the most difficult signals to reconstruct in terms of error bounds. We then show that by incorporating linear or non-linear mapping prior to sensing, significant improvement in the recovery performance can be achieved.

AB - In this paper, we first analyze impact of the distribution of sparse signals on reconstruction quality in compressive sensing through experimental results and heuristic analysis. We suggest that trinary/binary sparse signals are one of the most difficult signals to reconstruct in terms of error bounds. We then show that by incorporating linear or non-linear mapping prior to sensing, significant improvement in the recovery performance can be achieved.

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DO - 10.1109/DCC.2011.27

M3 - 会议稿件

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SN - 9780769543529

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BT - Proceedings - DCC 2011

T2 - 2011 Data Compression Conference, DCC 2011

Y2 - 29 March 2011 through 31 March 2011

ER -

A compressive sensing reconstruction algorithm for trinary and binary sparse signals using pre-mapping

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