Real-Time Minimization of the Piecewise Smooth Mumford-Shah Functional

We propose an algorithm for efficiently minimizing the piecewise smooth Mumford-Shah functional. The algorithm is based on an extension of a recent primal-dual algorithm from convex to non-convex optimization problems. The key idea is to rewrite the proximal operator in the primal-dual algorithm using Moreau’s identity. The resulting algorithm computes piecewise smooth approximations of color images at 15-20 frames per second at VGA resolution using GPU acceleration. Compared to convex relaxation approaches [18], it is orders of magnitude faster and does not require a discretization of color values. In contrast to the popular Ambrosio-Tortorelli approach [2], it naturally combines piecewise smooth and piecewise constant approximations, it does not require an epsilon-approximation and it is not based on an alternation scheme. The achieved energies are in practice at most 5% off the optimal value for one-dimensional problems. Numerous experiments demonstrate that the proposed algorithm is well-suited to perform discontinuity-preserving smoothing and real-time video cartooning.