Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/101504
Title: Variational structure–texture image decomposition on manifolds
Authors: Wu, Xiaoqun
Zheng, Jianmin
Wu, Chunlin
Cai, Yiyu
Keywords: DRNTU::Engineering::Computer science and engineering
Issue Date: 2013
Source: Wu, X., Zheng, J., Wu, C., & Cai, Y. (2013). Variational structure–texture image decomposition on manifolds. Signal processing, 93(7), 1773-1784.
Series/Report no.: Signal processing
Abstract: This paper considers the problem of decomposing an image defined on a manifold into a structural component and a textural component. We formulate such decomposition as a variational problem, in which the total variation energy is used for extracting the structural part and based on the properties of texture one of three norms, L2, L1 and G, is used in the fidelity term for the textural part. While L2 and G norms are used for texture of no a prior knowledge or oscillating pattern, L1 norm is used for structural or sparse texture. We develop efficient numerical methods to solve the proposed variational problems using augmented Lagrangian methods (ALM) when the manifold is represented by a triangular mesh. The contributions of the paper are two-fold: (1) We adapt the variational structure–texture image decomposition to manifolds, which takes the intrinsic property of manifolds into account. The non-quadratic fidelity terms with L1 and G norms are extended to 3D triangular meshes for the first time. (2) We show how to efficiently tackle the variational problems with non-linearity/non-differentiability terms by iteratively solving some sub-problems that either have closed form solutions or are to solve linear equations. We demonstrate the effectiveness of the proposed methods with examples and applications in detail enhancement and impulsive noise removal.
URI: https://hdl.handle.net/10356/101504
http://hdl.handle.net/10220/16833
ISSN: 0165-1684
DOI: 10.1016/j.sigpro.2013.01.019
Rights: © 2013 Elsevier B. V. This is the author created version of a work that has been peer reviewed and accepted for publication by Signal Processing, Elsevier B. V. It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at: [Article DOI: http://dx.doi.org/10.1016/j.sigpro.2013.01.019].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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