Fast compressive sensing of high-dimensional signals with tree-structure sparsity pattern

Chun Shien Lu; Wei Jie Liang

doi:10.1109/ChinaSIP.2014.6889342

Fast compressive sensing of high-dimensional signals with tree-structure sparsity pattern

Chun Shien Lu, Wei Jie Liang

Center for Teaching and Learning Development

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

6 Citations (Scopus)

Abstract

Compressive sensing of multi-dimensional signals (tensors) only receives limited attention. Separable sensing and proper sparsity pattern play two key roles for compressive sensing of tensors to be feasible and efficient. In view of inherent characteristic of 2D images and 3D videos, we propose the use of tree-structure sparsity pattern in tensor compressive sensing and develop a multiway tree-structure sparsity pattern OMP algorithm in this paper. Experimental results demonstrate the effectiveness of our method in terms of recovery quality and speed.

Original language	English
Title of host publication	2014 IEEE China Summit and International Conference on Signal and Information Processing, IEEE ChinaSIP 2014 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	738-742
Number of pages	5
ISBN (Electronic)	9781479954032
DOIs	https://doi.org/10.1109/ChinaSIP.2014.6889342
Publication status	Published - 2014 Sept 3
Event	2nd IEEE China Summit and International Conference on Signal and Information Processing, IEEE ChinaSIP 2014 - Xi'an, China Duration: 2014 Jul 9 → 2014 Jul 13

Publication series

Name	2014 IEEE China Summit and International Conference on Signal and Information Processing, IEEE ChinaSIP 2014 - Proceedings

Other

Other	2nd IEEE China Summit and International Conference on Signal and Information Processing, IEEE ChinaSIP 2014
Country/Territory	China
City	Xi'an
Period	14-07-09 → 14-07-13

All Science Journal Classification (ASJC) codes

Information Systems
Signal Processing

Access to Document

10.1109/ChinaSIP.2014.6889342

Cite this

Lu, C. S., & Liang, W. J. (2014). Fast compressive sensing of high-dimensional signals with tree-structure sparsity pattern. In 2014 IEEE China Summit and International Conference on Signal and Information Processing, IEEE ChinaSIP 2014 - Proceedings (pp. 738-742). Article 6889342 (2014 IEEE China Summit and International Conference on Signal and Information Processing, IEEE ChinaSIP 2014 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ChinaSIP.2014.6889342

Lu, Chun Shien ; Liang, Wei Jie. / Fast compressive sensing of high-dimensional signals with tree-structure sparsity pattern. 2014 IEEE China Summit and International Conference on Signal and Information Processing, IEEE ChinaSIP 2014 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2014. pp. 738-742 (2014 IEEE China Summit and International Conference on Signal and Information Processing, IEEE ChinaSIP 2014 - Proceedings).

@inproceedings{8fb7645f9f024818b6a176cd5aab1732,

title = "Fast compressive sensing of high-dimensional signals with tree-structure sparsity pattern",

abstract = "Compressive sensing of multi-dimensional signals (tensors) only receives limited attention. Separable sensing and proper sparsity pattern play two key roles for compressive sensing of tensors to be feasible and efficient. In view of inherent characteristic of 2D images and 3D videos, we propose the use of tree-structure sparsity pattern in tensor compressive sensing and develop a multiway tree-structure sparsity pattern OMP algorithm in this paper. Experimental results demonstrate the effectiveness of our method in terms of recovery quality and speed.",

author = "Lu, {Chun Shien} and Liang, {Wei Jie}",

note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2nd IEEE China Summit and International Conference on Signal and Information Processing, IEEE ChinaSIP 2014 ; Conference date: 09-07-2014 Through 13-07-2014",

year = "2014",

month = sep,

day = "3",

doi = "10.1109/ChinaSIP.2014.6889342",

language = "English",

series = "2014 IEEE China Summit and International Conference on Signal and Information Processing, IEEE ChinaSIP 2014 - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "738--742",

booktitle = "2014 IEEE China Summit and International Conference on Signal and Information Processing, IEEE ChinaSIP 2014 - Proceedings",

address = "United States",

}

Lu, CS & Liang, WJ 2014, Fast compressive sensing of high-dimensional signals with tree-structure sparsity pattern. in 2014 IEEE China Summit and International Conference on Signal and Information Processing, IEEE ChinaSIP 2014 - Proceedings., 6889342, 2014 IEEE China Summit and International Conference on Signal and Information Processing, IEEE ChinaSIP 2014 - Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 738-742, 2nd IEEE China Summit and International Conference on Signal and Information Processing, IEEE ChinaSIP 2014, Xi'an, China, 14-07-09. https://doi.org/10.1109/ChinaSIP.2014.6889342

Fast compressive sensing of high-dimensional signals with tree-structure sparsity pattern. / Lu, Chun Shien; Liang, Wei Jie.
2014 IEEE China Summit and International Conference on Signal and Information Processing, IEEE ChinaSIP 2014 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2014. p. 738-742 6889342 (2014 IEEE China Summit and International Conference on Signal and Information Processing, IEEE ChinaSIP 2014 - Proceedings).

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

TY - GEN

T1 - Fast compressive sensing of high-dimensional signals with tree-structure sparsity pattern

AU - Lu, Chun Shien

AU - Liang, Wei Jie

PY - 2014/9/3

Y1 - 2014/9/3

N2 - Compressive sensing of multi-dimensional signals (tensors) only receives limited attention. Separable sensing and proper sparsity pattern play two key roles for compressive sensing of tensors to be feasible and efficient. In view of inherent characteristic of 2D images and 3D videos, we propose the use of tree-structure sparsity pattern in tensor compressive sensing and develop a multiway tree-structure sparsity pattern OMP algorithm in this paper. Experimental results demonstrate the effectiveness of our method in terms of recovery quality and speed.

AB - Compressive sensing of multi-dimensional signals (tensors) only receives limited attention. Separable sensing and proper sparsity pattern play two key roles for compressive sensing of tensors to be feasible and efficient. In view of inherent characteristic of 2D images and 3D videos, we propose the use of tree-structure sparsity pattern in tensor compressive sensing and develop a multiway tree-structure sparsity pattern OMP algorithm in this paper. Experimental results demonstrate the effectiveness of our method in terms of recovery quality and speed.

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DO - 10.1109/ChinaSIP.2014.6889342

M3 - Conference contribution

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T3 - 2014 IEEE China Summit and International Conference on Signal and Information Processing, IEEE ChinaSIP 2014 - Proceedings

SP - 738

EP - 742

BT - 2014 IEEE China Summit and International Conference on Signal and Information Processing, IEEE ChinaSIP 2014 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2nd IEEE China Summit and International Conference on Signal and Information Processing, IEEE ChinaSIP 2014

Y2 - 9 July 2014 through 13 July 2014

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Lu CS, Liang WJ. Fast compressive sensing of high-dimensional signals with tree-structure sparsity pattern. In 2014 IEEE China Summit and International Conference on Signal and Information Processing, IEEE ChinaSIP 2014 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2014. p. 738-742. 6889342. (2014 IEEE China Summit and International Conference on Signal and Information Processing, IEEE ChinaSIP 2014 - Proceedings). doi: 10.1109/ChinaSIP.2014.6889342

Fast compressive sensing of high-dimensional signals with tree-structure sparsity pattern

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