PARAMETRIC MAXIMUM FLOW ALGORITHMS FOR FAST TOTAL VARIATION MINIMIZATION

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.