An Algorithm Based on Augmented Lagrangian Method for Generalized Gradient Vector Flow Computation

We propose a novel algorithm for the fast computation of generalized gradient vector flow (GGVF) whose high cost of computation has restricted its potential applications on images with large size. We reformulate the GGVF problem as a convex optimization model with equality constraint. Our approach is based on a variable splitting method to obtain an equivalent constrained optimization formulation, which is then addressed with the inexact augmented Lagrangian method (IALM). To further enhance the computational efficiency, IALM is incorporated in a multiresolution approach. Experiments on a set of images with a variety of sizes show that the proposed method can improve the computational speed of the original GGVF by one or two order of magnitude, and is comparable with the multigrid GGVF (MGGVF) method in terms of the computational efficiency.