Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/90961
Title: A nonlinear multigrid method for total variation minimization from image restoration
Authors: Chen, Ke
Tai, Xue Cheng
Keywords: DRNTU::Science::Mathematics::Applied mathematics::Numerical analysis
DRNTU::Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision
Issue Date: 2007
Source: Chen, K., & Tai, X. C. (2007). A nonlinear multigrid method for total variation minimization from image restoration. Journal of Scientific Computing, 33(2), 115-138.
Series/Report no.: Journal of scientific computing
Abstract: Image restoration has been an active research topic and variational formulations are particularly effective in high quality recovery. Although there exist many modelling and theoretical results, available iterative solvers are not yet robust in solving such modeling equations. Recent attempts on developing optimisation multigrid methods have been based on first order conditions. Different from this idea, this paper proposes to use piecewise linear function spanned subspace correction to design a multilevel method for directly solving the total variation minimisation. Our method appears to be more robust than the primal-dual method (Chan et al., SIAM J. Sci. Comput. 20(6), 1964–1977, 1999) previously found reliable. Supporting numerical results are presented.
URI: https://hdl.handle.net/10356/90961
http://hdl.handle.net/10220/4595
ISSN: 1573-7691
DOI: 10.1007/s10915-007-9145-9
Rights: Journal of Scientific @ copyright 2006 Computing Springer Verlag. The journal's website is located at http://www.springerlink.com/home/main.mpx
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SPMS Journal Articles

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