  Phase unwrapping is a key problem in many coherent imaging systems, such as
synthetic aperture radar (SAR) interferometry. A general formulation for
redundant integration of finite differences for phase unwrapping (Costantini et
al., 2010) was shown to produce a more reliable solution by exploiting
redundant differential estimates. However, this technique requires a commercial
linear programming solver for large-scale problems. For a linear cost function,
we propose a method based on Dual Decomposition that breaks the given problem
defined over a non-planar graph into tractable sub-problems over planar
subgraphs. We also propose a decomposition technique that exploits the
underlying graph structure for solving the sub-problems efficiently and
guarantees asymptotic convergence to the globally optimal solution. The
experimental results demonstrate that the proposed approach is comparable to
the existing state-of-the-art methods in terms of the estimate with a better
runtime and memory footprint.
