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

Veselin Dobrev, Jean-Luc Guermond, Bojan Popov

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


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.
Original languageEnglish (US)
Pages (from-to)1591-1616
Number of pages26
JournalSIAM Journal on Scientific Computing
Issue number3
StatePublished - Jan 2010
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): KUS-C1-016-04
Acknowledgements: 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).
This publication acknowledges KAUST support, but has no KAUST affiliated authors.


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