Title: New augmented Lagrangian method for a curvature dependent segmentation model

Abstract: Augmented Lagrangian methods (ALMs) have proved to be successful for the minimization of curvature dependent functionals in image processing. However, those ALM based algorithms often suffer from choosing appropriate penalty parameters in the numerical implementation. In this talk, we will discuss our recent work on the development of ALM based algorithms for a curvature based segmentation model by introducing fewer Lagrange multipliers. Besides significantly reducing the effort of selecting suitable penalty parameters, the new algorithms help capture curvature more faithfully than those existing ones. Numerical experiments will also be presented to valid the effectiveness of the proposed algorithms.