Surface Reconstruction and Image Enhancement via $L^1$-Minimization

Handle URI:
http://hdl.handle.net/10754/599816
Title:
Surface Reconstruction and Image Enhancement via $L^1$-Minimization
Authors:
Dobrev, Veselin; Guermond, Jean-Luc; Popov, Bojan
Abstract:
A surface reconstruction technique based on minimization of the total variation of the gradient is introduced. Convergence of the method is established, and an interior-point algorithm solving the associated linear programming problem is introduced. The reconstruction algorithm is illustrated on various test cases including natural and urban terrain data, and enhancement oflow-resolution or aliased images. Copyright © by SIAM.
Citation:
Dobrev V, Guermond J-L, Popov B (2010) Surface Reconstruction and Image Enhancement via $L^1$-Minimization. SIAM Journal on Scientific Computing 32: 1591–1616. Available: http://dx.doi.org/10.1137/09075408X.
Publisher:
Society for Industrial & Applied Mathematics (SIAM)
Journal:
SIAM Journal on Scientific Computing
KAUST Grant Number:
KUS-C1-016-04
Issue Date:
Jan-2010
DOI:
10.1137/09075408X
Type:
Article
ISSN:
1064-8275; 1095-7197
Sponsors:
Received by the editors March 26, 2009; accepted for publication ( in revised form) February 12, 2010; published electronically June 9, 2010. This material is based upon work supported by the National Science Foundation grants DMS-0510650 and DMS-0811041. This publication is based on work partially supported by Award KUS-C1-016-04, made by King Abdullah University of Science and Technology (KAUST).
Appears in Collections:
Publications Acknowledging KAUST Support

Full metadata record

DC FieldValue Language
dc.contributor.authorDobrev, Veselinen
dc.contributor.authorGuermond, Jean-Lucen
dc.contributor.authorPopov, Bojanen
dc.date.accessioned2016-02-28T06:10:28Zen
dc.date.available2016-02-28T06:10:28Zen
dc.date.issued2010-01en
dc.identifier.citationDobrev V, Guermond J-L, Popov B (2010) Surface Reconstruction and Image Enhancement via $L^1$-Minimization. SIAM Journal on Scientific Computing 32: 1591–1616. Available: http://dx.doi.org/10.1137/09075408X.en
dc.identifier.issn1064-8275en
dc.identifier.issn1095-7197en
dc.identifier.doi10.1137/09075408Xen
dc.identifier.urihttp://hdl.handle.net/10754/599816en
dc.description.abstractA surface reconstruction technique based on minimization of the total variation of the gradient is introduced. Convergence of the method is established, and an interior-point algorithm solving the associated linear programming problem is introduced. The reconstruction algorithm is illustrated on various test cases including natural and urban terrain data, and enhancement oflow-resolution or aliased images. Copyright © by SIAM.en
dc.description.sponsorshipReceived by the editors March 26, 2009; accepted for publication ( in revised form) February 12, 2010; published electronically June 9, 2010. This material is based upon work supported by the National Science Foundation grants DMS-0510650 and DMS-0811041. This publication is based on work partially supported by Award KUS-C1-016-04, made by King Abdullah University of Science and Technology (KAUST).en
dc.publisherSociety for Industrial & Applied Mathematics (SIAM)en
dc.subjectData reconstruction digital elevation modelsen
dc.subjectFinite elementsen
dc.subjectImage enhancementen
dc.subjectInterior-point algorithmen
dc.subjectL1-minimizationen
dc.titleSurface Reconstruction and Image Enhancement via $L^1$-Minimizationen
dc.typeArticleen
dc.identifier.journalSIAM Journal on Scientific Computingen
dc.contributor.institutionLawrence Livermore National Laboratory, Livermore, United Statesen
dc.contributor.institutionTexas A and M University, College Station, United Statesen
dc.contributor.institutionLIMSI Laobratoire d'Informatique pour la Mecanique et les Sciences de l'Ingenieur, Orsay, Franceen
kaust.grant.numberKUS-C1-016-04en
All Items in KAUST are protected by copyright, with all rights reserved, unless otherwise indicated.