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

教學發展中心

研究成果: Conference contribution

6 引文斯高帕斯（Scopus）

摘要

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.

原文	English
主出版物標題	2014 IEEE China Summit and International Conference on Signal and Information Processing, IEEE ChinaSIP 2014 - Proceedings
發行者	Institute of Electrical and Electronics Engineers Inc.
頁面	738-742
頁數	5
ISBN（電子）	9781479954032
DOIs	https://doi.org/10.1109/ChinaSIP.2014.6889342
出版狀態	Published - 2014 9月 3
事件	2nd IEEE China Summit and International Conference on Signal and Information Processing, IEEE ChinaSIP 2014 - Xi'an, China 持續時間: 2014 7月 9 → 2014 7月 13

出版系列

名字	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
國家/地區	China
城市	Xi'an
期間	14-07-09 → 14-07-13

All Science Journal Classification (ASJC) codes

資訊系統
訊號處理

存取文件

10.1109/ChinaSIP.2014.6889342

其它文件與鏈接

引用此

Lu, C. S., & Liang, W. J. (2014). Fast compressive sensing of high-dimensional signals with tree-structure sparsity pattern. 於 2014 IEEE China Summit and International Conference on Signal and Information Processing, IEEE ChinaSIP 2014 - Proceedings (頁 738-742). 文章 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. 頁 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. 於 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., 頁 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).

研究成果: 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.

UR - http://www.scopus.com/inward/record.url?scp=84929392391&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84929392391&partnerID=8YFLogxK

U2 - 10.1109/ChinaSIP.2014.6889342

DO - 10.1109/ChinaSIP.2014.6889342

M3 - Conference contribution

AN - SCOPUS:84929392391

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

ER -

Lu CS, Liang WJ. Fast compressive sensing of high-dimensional signals with tree-structure sparsity pattern. 於 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

摘要

出版系列

Other

All Science Journal Classification (ASJC) codes

存取文件

其它文件與鏈接

指紋

引用此