Block-based variable density compressed image sampling

Wei Qiao; Bin Liu; Zixiang Xiong; Gonzalo R. Arce; Javier Garcia-Frias; Wenwu Zhu; Zhisheng Yan

doi:10.1109/ICIP.2012.6467008

Block-based variable density compressed image sampling

Wei Qiao
, Bin Liu
, Zixiang Xiong
, Gonzalo R. Arce
, Javier Garcia-Frias
, Wenwu Zhu
, Zhisheng Yan

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

5 Citations (Scopus)

Abstract

Compressed sampling (CS) is a technique that enables signal reconstruction at sub-Nyquist sampling rate. A key problem in CS is how to design the sampling scheme. In this paper, we propose a novel sampling method for compressed image sampling, which exploits a priori information and uses a block-based strategy to improve image reconstruction. Our block-based sampling scheme assigns more samples to blocks with more high-frequency contents while making sure that important coefficients of each block are sampled. Simulation results show that our proposed method outperforms existing methods on both reconstruction quality and running time.

Original language	English
Title of host publication	2012 IEEE International Conference on Image Processing, ICIP 2012 - Proceedings
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	909-912
Number of pages	4
ISBN (Print)	9781467325332
DOIs	https://doi.org/10.1109/ICIP.2012.6467008
Publication status	Published - 2012
Externally published	Yes
Event	IEEE International Conference on Image Processing 2012 - Coronado Springs - Disney World, Orlando, United States of America Duration: 30 Sept 2012 → 3 Oct 2012 Conference number: 19th https://ieeexplore.ieee.org/xpl/conhome/6451323/proceeding (Proceedings)

Conference

Conference	IEEE International Conference on Image Processing 2012
Abbreviated title	ICIP 2012
Country/Territory	United States of America
City	Orlando
Period	30/09/12 → 3/10/12
Internet address	https://ieeexplore.ieee.org/xpl/conhome/6451323/proceeding (Proceedings)

Keywords

block-based
Compressed sensing
image reconstruction
variable density sampling

Access to Document

10.1109/ICIP.2012.6467008

Cite this

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title = "Block-based variable density compressed image sampling",

abstract = "Compressed sampling (CS) is a technique that enables signal reconstruction at sub-Nyquist sampling rate. A key problem in CS is how to design the sampling scheme. In this paper, we propose a novel sampling method for compressed image sampling, which exploits a priori information and uses a block-based strategy to improve image reconstruction. Our block-based sampling scheme assigns more samples to blocks with more high-frequency contents while making sure that important coefficients of each block are sampled. Simulation results show that our proposed method outperforms existing methods on both reconstruction quality and running time.",

keywords = "block-based, Compressed sensing, image reconstruction, variable density sampling",

author = "Wei Qiao and Bin Liu and Zixiang Xiong and Arce, \{Gonzalo R.\} and Javier Garcia-Frias and Wenwu Zhu and Zhisheng Yan",

year = "2012",

doi = "10.1109/ICIP.2012.6467008",

language = "English",

isbn = "9781467325332",

pages = "909--912",

booktitle = "2012 IEEE International Conference on Image Processing, ICIP 2012 - Proceedings",

publisher = "IEEE, Institute of Electrical and Electronics Engineers",

address = "United States of America",

note = "IEEE International Conference on Image Processing 2012, ICIP 2012 ; Conference date: 30-09-2012 Through 03-10-2012",

url = "https://ieeexplore.ieee.org/xpl/conhome/6451323/proceeding",

}

Qiao, W, Liu, B, Xiong, Z, Arce, GR, Garcia-Frias, J, Zhu, W & Yan, Z 2012, Block-based variable density compressed image sampling. in 2012 IEEE International Conference on Image Processing, ICIP 2012 - Proceedings. IEEE, Institute of Electrical and Electronics Engineers, pp. 909-912, IEEE International Conference on Image Processing 2012, Orlando, Florida, United States of America, 30/09/12. https://doi.org/10.1109/ICIP.2012.6467008

TY - GEN

T1 - Block-based variable density compressed image sampling

AU - Qiao, Wei

AU - Liu, Bin

AU - Xiong, Zixiang

AU - Arce, Gonzalo R.

AU - Garcia-Frias, Javier

AU - Zhu, Wenwu

AU - Yan, Zhisheng

N1 - Conference code: 19th

PY - 2012

Y1 - 2012

N2 - Compressed sampling (CS) is a technique that enables signal reconstruction at sub-Nyquist sampling rate. A key problem in CS is how to design the sampling scheme. In this paper, we propose a novel sampling method for compressed image sampling, which exploits a priori information and uses a block-based strategy to improve image reconstruction. Our block-based sampling scheme assigns more samples to blocks with more high-frequency contents while making sure that important coefficients of each block are sampled. Simulation results show that our proposed method outperforms existing methods on both reconstruction quality and running time.

AB - Compressed sampling (CS) is a technique that enables signal reconstruction at sub-Nyquist sampling rate. A key problem in CS is how to design the sampling scheme. In this paper, we propose a novel sampling method for compressed image sampling, which exploits a priori information and uses a block-based strategy to improve image reconstruction. Our block-based sampling scheme assigns more samples to blocks with more high-frequency contents while making sure that important coefficients of each block are sampled. Simulation results show that our proposed method outperforms existing methods on both reconstruction quality and running time.

KW - block-based

KW - Compressed sensing

KW - image reconstruction

KW - variable density sampling

UR - https://www.scopus.com/pages/publications/84875835137

U2 - 10.1109/ICIP.2012.6467008

DO - 10.1109/ICIP.2012.6467008

M3 - Conference Paper

AN - SCOPUS:84875835137

SN - 9781467325332

SP - 909

EP - 912

BT - 2012 IEEE International Conference on Image Processing, ICIP 2012 - Proceedings

PB - IEEE, Institute of Electrical and Electronics Engineers

T2 - IEEE International Conference on Image Processing 2012

Y2 - 30 September 2012 through 3 October 2012

ER -

Block-based variable density compressed image sampling

Abstract

Conference

Keywords

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