Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/97108
Title: Adaptive wavelet collocation methods for image segmentation using TV–Allen–Cahn type models
Authors: Tai, Xue Cheng
Rong, Zhijian
Wang, Li-Lian
Keywords: DRNTU::Science::Mathematics
Issue Date: 2013
Source: Rong, Z., Wang, L.-L., & Tai, X.-C. (2013). Adaptive wavelet collocation methods for image segmentation using TV–Allen–Cahn type models. Advances in computational mathematics, 38(1), 101-131.
Series/Report no.: Advances in computational mathematics
Abstract: An adaptive wavelet-based method is proposed for solving TV(total variation)–Allen–Cahn type models for multi-phase image segmentation. The adaptive algorithm integrates (i) grid adaptation based on a threshold of the sparse wavelet representation of the locally-structured solution; and (ii) effective finite difference on irregular stencils. The compactly supported interpolating-type wavelets enjoy very fast wavelet transforms, and act as a piecewise constant function filter. These lead to fairly sparse computational grids, and relax the stiffness of the nonlinear PDEs. Equipped with this algorithm, the proposed sharp interface model becomes very effective for multi-phase image segmentation. This method is also applied to image restoration and similar advantages are observed.
URI: https://hdl.handle.net/10356/97108
http://hdl.handle.net/10220/17921
DOI: http://dx.doi.org/10.1007/s10444-011-9227-y
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SPMS Journal Articles

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