dc.contributor.author |
Lee, William |
en |
dc.contributor.author |
Eason, D |
en |
dc.contributor.author |
Andrews, Mark |
en |
dc.coverage.spatial |
Dunedin, New Zealand |
en |
dc.date.accessioned |
2016-06-10T04:36:50Z |
en |
dc.date.issued |
2012-12 |
en |
dc.identifier.citation |
Proceedings of the 19th Electronics New Zealand Conference, 2012, pp. 91 - 96 |
en |
dc.identifier.isbn |
9780473232757 |
en |
dc.identifier.uri |
http://hdl.handle.net/2292/29024 |
en |
dc.description.abstract |
Compressive sensing is a recently introduced signal acquisition technique that can significantly reduce the number of measurements necessary for reconstructing a signal to a high degree of accuracy. This has considerable practical benefits to hyperspectral imaging where the current cost and effort required to collect the vast amount of data hinders its applicability to numerous field of sciences. Current state-of-the-art algorithms for compressive sensing are designed for solving problems with single regularisers (such as the sparse-inducing L1-norm and total variation). This paper presents a method for solving compressive hyperspectral image sensing problem using joint L1-norm and total variation regularisation as an on-going effort in improving compressive hyperspectral image restoration. Preliminary results suggest that such regularisation gives better results than using the individual regularisers alone. Current work in progress includes incorporating spatial-spectral regularisation for hyperspectral image recovery and analysing new compressive sampling schemes to exploit such regularisation. |
en |
dc.description.uri |
http://enzcon.org.nz/ |
en |
dc.publisher |
University of Otago |
en |
dc.relation.ispartof |
19th Electronics New Zealand Conference (ENZCon) 2012 |
en |
dc.relation.ispartofseries |
Proceedings of the 19th Electronics New Zealand Conference |
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dc.rights |
Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher. Details obtained from http://enzcon.org.nz/Past_Proceedings/Proceedings_2012.pdf |
en |
dc.rights.uri |
https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm |
en |
dc.title |
Compressive Hyperspectral Image Sensing Restoration via Joint TV-L1 Regularisation |
en |
dc.type |
Conference Item |
en |
pubs.begin-page |
91 |
en |
dc.rights.holder |
Copyright: The Authors |
en |
pubs.author-url |
http://www.physics.otago.ac.nz/reports/electronics/ENZCon2012.pdf |
en |
pubs.end-page |
96 |
en |
pubs.finish-date |
2012-12-12 |
en |
pubs.start-date |
2012-12-10 |
en |
dc.rights.accessrights |
http://purl.org/eprint/accessRights/RestrictedAccess |
en |
pubs.subtype |
Proceedings |
en |
pubs.elements-id |
528979 |
en |
pubs.org-id |
Engineering |
en |
pubs.org-id |
Department of Electrical, Computer and Software Engineering |
en |
pubs.record-created-at-source-date |
2016-05-26 |
en |