Nonlinear image reconstruction in block-based compressive imaging

Jun Ke; Edmund Y. Lam

doi:10.1109/ISCAS.2012.6271926

Nonlinear image reconstruction in block-based compressive imaging

Jun Ke^*
, Edmund Y. Lam

^*Corresponding author for this work

The University of Hong Kong

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

3 Citations (Scopus)

Abstract

A block-based compressive imaging (BCI) system with sequential architecture is presented in this paper. Feature measurements are collected using the principal component analysis (PCA) projection vectors. Then, we discuss an object prior learning framework based on the Field-of-Expert (FoE) model, and provide its implementation in the BCI reconstruction problem. Experimental results are used to demonstrate the reconstruction performance of the FoE-based method.

Original language	English
Title of host publication	ISCAS 2012 - 2012 IEEE International Symposium on Circuits and Systems
Publisher	IEEE Computer Society
Pages	2917-2920
Number of pages	4
ISBN (Electronic)	9781467302197
DOIs	https://doi.org/10.1109/ISCAS.2012.6271926
Publication status	Published - 2012
Externally published	Yes
Event	2012 IEEE International Symposium on Circuits and Systems, ISCAS 2012 - Seoul, Korea, Republic of Duration: 20 May 2012 → 23 May 2012

Publication series

Name	ISCAS 2012 - 2012 IEEE International Symposium on Circuits and Systems

Conference

Conference	2012 IEEE International Symposium on Circuits and Systems, ISCAS 2012
Country/Territory	Korea, Republic of
City	Seoul
Period	20/05/12 → 23/05/12

Access to Document

10.1109/ISCAS.2012.6271926

Cite this

@inproceedings{4f6d657535374db2844e3956cee84b99,

title = "Nonlinear image reconstruction in block-based compressive imaging",

abstract = "A block-based compressive imaging (BCI) system with sequential architecture is presented in this paper. Feature measurements are collected using the principal component analysis (PCA) projection vectors. Then, we discuss an object prior learning framework based on the Field-of-Expert (FoE) model, and provide its implementation in the BCI reconstruction problem. Experimental results are used to demonstrate the reconstruction performance of the FoE-based method.",

author = "Jun Ke and Lam, \{Edmund Y.\}",

year = "2012",

doi = "10.1109/ISCAS.2012.6271926",

language = "English",

series = "ISCAS 2012 - 2012 IEEE International Symposium on Circuits and Systems",

publisher = "IEEE Computer Society",

pages = "2917--2920",

booktitle = "ISCAS 2012 - 2012 IEEE International Symposium on Circuits and Systems",

address = "United States",

note = "2012 IEEE International Symposium on Circuits and Systems, ISCAS 2012 ; Conference date: 20-05-2012 Through 23-05-2012",

}

Ke, J & Lam, EY 2012, Nonlinear image reconstruction in block-based compressive imaging. in ISCAS 2012 - 2012 IEEE International Symposium on Circuits and Systems., 6271926, ISCAS 2012 - 2012 IEEE International Symposium on Circuits and Systems, IEEE Computer Society, pp. 2917-2920, 2012 IEEE International Symposium on Circuits and Systems, ISCAS 2012, Seoul, Korea, Republic of, 20/05/12. https://doi.org/10.1109/ISCAS.2012.6271926

Nonlinear image reconstruction in block-based compressive imaging. / Ke, Jun; Lam, Edmund Y.
ISCAS 2012 - 2012 IEEE International Symposium on Circuits and Systems. IEEE Computer Society, 2012. p. 2917-2920 6271926 (ISCAS 2012 - 2012 IEEE International Symposium on Circuits and Systems).

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

TY - GEN

T1 - Nonlinear image reconstruction in block-based compressive imaging

AU - Ke, Jun

AU - Lam, Edmund Y.

PY - 2012

Y1 - 2012

N2 - A block-based compressive imaging (BCI) system with sequential architecture is presented in this paper. Feature measurements are collected using the principal component analysis (PCA) projection vectors. Then, we discuss an object prior learning framework based on the Field-of-Expert (FoE) model, and provide its implementation in the BCI reconstruction problem. Experimental results are used to demonstrate the reconstruction performance of the FoE-based method.

AB - A block-based compressive imaging (BCI) system with sequential architecture is presented in this paper. Feature measurements are collected using the principal component analysis (PCA) projection vectors. Then, we discuss an object prior learning framework based on the Field-of-Expert (FoE) model, and provide its implementation in the BCI reconstruction problem. Experimental results are used to demonstrate the reconstruction performance of the FoE-based method.

UR - https://www.scopus.com/pages/publications/84866624413

U2 - 10.1109/ISCAS.2012.6271926

DO - 10.1109/ISCAS.2012.6271926

M3 - Conference contribution

AN - SCOPUS:84866624413

T3 - ISCAS 2012 - 2012 IEEE International Symposium on Circuits and Systems

SP - 2917

EP - 2920

BT - ISCAS 2012 - 2012 IEEE International Symposium on Circuits and Systems

PB - IEEE Computer Society

T2 - 2012 IEEE International Symposium on Circuits and Systems, ISCAS 2012

Y2 - 20 May 2012 through 23 May 2012

ER -

Nonlinear image reconstruction in block-based compressive imaging

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