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: Contribution to conference › Paper › 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
Pages	2917-2920
Number of pages	4
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

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

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10.1109/ISCAS.2012.6271926

Cite this

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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.}",

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doi = "10.1109/ISCAS.2012.6271926",

language = "English",

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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 - http://www.scopus.com/inward/record.url?scp=84866624413&partnerID=8YFLogxK

U2 - 10.1109/ISCAS.2012.6271926

DO - 10.1109/ISCAS.2012.6271926

M3 - Paper

AN - SCOPUS:84866624413

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EP - 2920

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

Abstract

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