Efficient parallel multigrid relaxation algorithms for Markov random field-based low-level vision applications

We present a new algorithmic framework which enables making a full use of the large potential of data parallelism available on 2D processor arrays for the implementation of nonlinear multigrid relaxation methods. This framework leads to fast convergence towards quasi-optimal solutions. It is demonstrated on two different low-level vision applications. >