Visual reconstruction
Comparison of the Efficiency of Deterministic and Stochastic Algorithms for Visual Reconstruction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Constrained Restoration and the Recovery of Discontinuities
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient graph-based energy minimization methods in computer vision
Efficient graph-based energy minimization methods in computer vision
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
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Energy Minimization via Graph Cuts: Settling What is Possible
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
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
Exact optimization for Markov random fields with convex priors
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Phase Unwrapping via Graph Cuts
IEEE Transactions on Image Processing
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We present 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 synthetic aperture radar/sonar (InSAR/SAS), magnetic resonance imaging (MRI), and optical interferometry. We adopt a discrete energy minimization viewpoint, where the objective function is a first-order Markov random field. The minimization problem is dealt with via a binary iterative scheme, with each iteration step cast onto a graph cut based optimization problem. For convex clique potentials we provide an exact energy minimization algorithm; namely we solve exactly the PU classical Lp norm, with p ≥ 1. For nonconvex clique potentials, it is well known that PU performance is particularly enhanced, namely, the discontinuity preserving ability; however the problem is NP-hard. Accordingly, we provide an approximate algorithm, which is a modified version of the first proposed one. For simplicity we call both algorithms PUMF, for Phase Unwrapping Max-Flow. The state-of-the-art competitiveness of PUMF is illustrated in a series of experiments.