Spotlight-Mode Synthetic Aperture Radar: A Signal Processing Approach
Spotlight-Mode Synthetic Aperture Radar: A Signal Processing Approach
What Energy Functions Can Be Minimizedvia Graph Cuts?
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bayesian approaches to phase unwrapping: theoretical study
IEEE Transactions on Signal Processing
Model based phase unwrapping of 2-D signals
IEEE Transactions on Signal Processing
Absolute phase image reconstruction: a stochastic nonlinear filtering approach
IEEE Transactions on Image Processing
The ZπM algorithm: a method for interferometric image reconstruction in SAR/SAS
IEEE Transactions on Image Processing
New algorithms for convex cost tension problem with application to computer vision
Discrete Optimization
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This paper presents a new algorithm for recovering the absolute phase from modulo-2π phase, the so-called phase unwrapping (PU) problem. PU arises as a key step in several imaging technologies, from which we emphasize interferometric SAR and SAS, where topography is inferred from absolute phase measurements between two (or more) antennas and the terrain itself. The adopted criterion is the minimization of the Lp norm of phase differences [1], [2], usually leading to computationally demanding algorithms. Our approach follows the idea introduced in [3] of an iterative binary optimization scheme, the novelty being the casting onto a graph max-flow/min-cut formulation, for which there exists efficient algorithms. That graph formulation is based on recent energy minimization results via graph-cuts [4]. Accordingly, we term this new algorithm PUMF (for phase unwrapping max-flow). A set of experimental results illustrates the effectiveness of PUMF.