Compressive large-scale image sensing

Wei Jie Liang; Gang Xuan Lin; Chun Shien Lu

doi:10.1109/GlobalSIP.2015.7418221

Compressive large-scale image sensing

Wei Jie Liang, Gang Xuan Lin, Chun Shien Lu

教學發展中心

研究成果: Conference contribution

1 引文斯高帕斯（Scopus）

摘要

Cost-efficient compressive sensing of large-scale images with fast reconstructed high-quality results is very challenging. In this paper, we propose a new compressive large-scale image sensing method, composed of operator-based strategy in the context of fixed point continuation technique and weighted LASSO with tree structure sparsity pattern. The main characteristic of our method is free from any assumptions and restrictions. The feasibility of our method is verified via computational complexity and convergence analyses, extensive simulations, and comparisons with state-of-the-art algorithms.

原文	English
主出版物標題	2015 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2015
發行者	Institute of Electrical and Electronics Engineers Inc.
頁面	378-382
頁數	5
ISBN（電子）	9781479975914
DOIs	https://doi.org/10.1109/GlobalSIP.2015.7418221
出版狀態	Published - 2016 2月 23
事件	IEEE Global Conference on Signal and Information Processing, GlobalSIP 2015 - Orlando, United States 持續時間: 2015 12月 13 → 2015 12月 16

出版系列

名字	2015 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2015

Other

Other	IEEE Global Conference on Signal and Information Processing, GlobalSIP 2015
國家/地區	United States
城市	Orlando
期間	15-12-13 → 15-12-16

All Science Journal Classification (ASJC) codes

資訊系統
訊號處理

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10.1109/GlobalSIP.2015.7418221

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引用此

Liang, W. J., Lin, G. X., & Lu, C. S. (2016). Compressive large-scale image sensing. 於 2015 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2015 (頁 378-382). 文章 7418221 (2015 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2015). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/GlobalSIP.2015.7418221

@inproceedings{7614571dbccb47739c4264fbf00869ba,

title = "Compressive large-scale image sensing",

abstract = "Cost-efficient compressive sensing of large-scale images with fast reconstructed high-quality results is very challenging. In this paper, we propose a new compressive large-scale image sensing method, composed of operator-based strategy in the context of fixed point continuation technique and weighted LASSO with tree structure sparsity pattern. The main characteristic of our method is free from any assumptions and restrictions. The feasibility of our method is verified via computational complexity and convergence analyses, extensive simulations, and comparisons with state-of-the-art algorithms.",

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

note = "Publisher Copyright: {\textcopyright} 2015 IEEE. Copyright: Copyright 2016 Elsevier B.V., All rights reserved.; IEEE Global Conference on Signal and Information Processing, GlobalSIP 2015 ; Conference date: 13-12-2015 Through 16-12-2015",

year = "2016",

month = feb,

day = "23",

doi = "10.1109/GlobalSIP.2015.7418221",

language = "English",

series = "2015 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2015",

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

pages = "378--382",

booktitle = "2015 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2015",

address = "United States",

}

Liang, WJ, Lin, GX & Lu, CS 2016, Compressive large-scale image sensing. 於 2015 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2015., 7418221, 2015 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2015, Institute of Electrical and Electronics Engineers Inc., 頁 378-382, IEEE Global Conference on Signal and Information Processing, GlobalSIP 2015, Orlando, United States, 15-12-13. https://doi.org/10.1109/GlobalSIP.2015.7418221

Compressive large-scale image sensing. / Liang, Wei Jie; Lin, Gang Xuan; Lu, Chun Shien.
2015 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2015. Institute of Electrical and Electronics Engineers Inc., 2016. p. 378-382 7418221 (2015 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2015).

研究成果: Conference contribution

TY - GEN

T1 - Compressive large-scale image sensing

AU - Liang, Wei Jie

AU - Lin, Gang Xuan

AU - Lu, Chun Shien

PY - 2016/2/23

Y1 - 2016/2/23

N2 - Cost-efficient compressive sensing of large-scale images with fast reconstructed high-quality results is very challenging. In this paper, we propose a new compressive large-scale image sensing method, composed of operator-based strategy in the context of fixed point continuation technique and weighted LASSO with tree structure sparsity pattern. The main characteristic of our method is free from any assumptions and restrictions. The feasibility of our method is verified via computational complexity and convergence analyses, extensive simulations, and comparisons with state-of-the-art algorithms.

AB - Cost-efficient compressive sensing of large-scale images with fast reconstructed high-quality results is very challenging. In this paper, we propose a new compressive large-scale image sensing method, composed of operator-based strategy in the context of fixed point continuation technique and weighted LASSO with tree structure sparsity pattern. The main characteristic of our method is free from any assumptions and restrictions. The feasibility of our method is verified via computational complexity and convergence analyses, extensive simulations, and comparisons with state-of-the-art algorithms.

UR - http://www.scopus.com/inward/record.url?scp=84964788316&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84964788316&partnerID=8YFLogxK

U2 - 10.1109/GlobalSIP.2015.7418221

DO - 10.1109/GlobalSIP.2015.7418221

M3 - Conference contribution

AN - SCOPUS:84964788316

T3 - 2015 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2015

SP - 378

EP - 382

BT - 2015 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2015

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - IEEE Global Conference on Signal and Information Processing, GlobalSIP 2015

Y2 - 13 December 2015 through 16 December 2015

ER -

Compressive large-scale image sensing

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All Science Journal Classification (ASJC) codes

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