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 language||English (US)|
|Number of pages||26|
|Journal||SIAM Journal on Scientific Computing|
|State||Published - Jan 2010|
Bibliographical noteKAUST 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.