PURE: Scalable Phase Unwrapping with Spatial Redundant Arcs
This work addresses scalability issues in phase unwrapping for applications like SAR interferometry, offering an incremental improvement over existing methods.
The paper tackles the phase unwrapping problem in coherent imaging systems by proposing a method based on Dual Decomposition that breaks the problem into tractable sub-problems, achieving comparable estimates to state-of-the-art methods with better runtime and memory footprint.
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.