Numerical Methods for Non-Smooth $L^1$ Optimization: Applications to Free Surface Flows and Image Denoising
Year: 2009
International Journal of Numerical Analysis and Modeling, Vol. 6 (2009), Iss. 3 : pp. 355–374
Abstract
Non-smooth optimization problems based on $L^1$ norms are investigated for smoothing of signals with noise or functions with sharp gradients. The use of $L^1$ norms allows to reduce the blurring introduced by methods based on $L^2$ norms. Numerical methods based on over-relaxation and augmented Lagrangian algorithms are proposed. Applications to free surface flows and image denoising are presented.
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Journal Article Details
Publisher Name: Global Science Press
Language: English
DOI: https://doi.org/2009-IJNAM-772
International Journal of Numerical Analysis and Modeling, Vol. 6 (2009), Iss. 3 : pp. 355–374
Published online: 2009-01
AMS Subject Headings: Global Science Press
Copyright: COPYRIGHT: © Global Science Press
Pages: 20
Keywords: $L^1$ optimization over-relaxation algorithm augmented Lagrangian methods smoothing image denoising.