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Iterative weighted DCT-SVD for compressive imaging

conference contribution
posted on 18.12.2018, 00:00 by Zhenglin WangZhenglin Wang, I Lee
This paper proposes iterative weighted discrete cosine transform and singular value decomposition (DCT-SVD) transform for compressive sensing (CS) reconstruction. The idea of weight utilizes the priori that the components of the transform representation of an image usually are unequally important. Sequentially, larger weights are assigned to more important components to improve reconstruction quality. Besides, iterative DCT-SVD can be regarded as a sequence of adaptive transforms. DCT starts a recovery procedure as an initial transform. SVD is then performed on previous reconstruction to obtain a pair of transform bases for next recovery, and the mechanism is repeated until the reconstructions remain unchanged. The proposal does not introduce extra cost to CS sampling, but improves reconstruction quality much according to the numerical simulations. © 2015 IEEE.

History

Parent Title

Proceedings: 2015 International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2015

Start Page

405

End Page

408

Number of Pages

4

Start Date

23/09/2015

Finish Date

25/09/2015

ISBN-13

9781509001880

Location

Adelaide, S.A., Australia

Publisher

IEEE

Place of Publication

Piscataway, NJ

Peer Reviewed

Yes

Open Access

No

External Author Affiliations

University of South Australia

Era Eligible

Yes

Name of Conference

2015 International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP)