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• W2093133114 abstract "This report studies the global minimization of anisotropically discretized total variation (TV) energies with an $L^p$ (in particular, $L^1$ and $L^2$) fidelity term using parametric maximum flow algorithms to minimize s-t cut representations of these energies. The TV/$L^2$ model, also known as the Rudin–Osher–Fatemi (ROF) model, is suitable for restoring images contaminated by Gaussian noise, while the TV/$L^1$ model is able to remove impulsive noise from grayscale images and perform multiscale decompositions of them. Preliminary numerical results on large-scale two-dimensional CT and three-dimensional brain MR images are presented to illustrate the effectiveness of these approaches." @default.
• W2093133114 created "2016-06-24" @default.
• W2093133114 creator A5027297328 @default.
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• W2093133114 date "2009-01-01" @default.
• W2093133114 modified "2023-09-23" @default.
• W2093133114 title "Parametric Maximum Flow Algorithms for Fast Total Variation Minimization" @default.
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• W2093133114 doi "https://doi.org/10.1137/070706318" @default.
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