Multi-frequency Phase Unwrapping from Noisy Data: Adaptive Local Maximum Likelihood Approach
SCIA '09 Proceedings of the 16th Scandinavian Conference on Image Analysis
Global optimization for first order Markov Random Fields with submodular priors
Discrete Applied Mathematics
Iterated conditional modes for inverse dithering
Signal Processing
SAR image regularization with fast approximate discrete minimization
IEEE Transactions on Image Processing
Noise-Residue Filtering Based on Unsupervised Clustering for Phase Unwrapping
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part II
Fast coupled path planning: from pseudo-polynomial to polynomial
COCOON'10 Proceedings of the 16th annual international conference on Computing and combinatorics
Markov random field based phase demodulation of interferometric images
Computer Vision and Image Understanding
Discontinuity preserving phase unwrapping using graph cuts
EMMCVPR'05 Proceedings of the 5th international conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
Graph-Cut Energy Minimization for Object Extraction in MRCP Medical Images
Journal of Medical Systems
New algorithms for convex cost tension problem with application to computer vision
Discrete Optimization
Review article: Multilabel partition moves for MRF optimization
Image and Vision Computing
Phase Unwrapping Using Energy Minimization Methods for MRI Phase Image
International Journal of E-Health and Medical Communications
Iterative phase unwrapping in color doppler flow mapping
IScIDE'12 Proceedings of the third Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
| Hi-index | 0.01 |
Phase unwrapping is the inference of absolute phase from modulo-2pi phase. This paper introduces a new energy minimization framework for phase unwrapping. The considered objective functions are first-order Markov random fields. We provide an exact energy minimization algorithm, whenever the corresponding clique potentials are convex, namely for the phase unwrapping classical Lp norm, with pges1. Its complexity is KT(n,3n), where K is the length of the absolute phase domain measured in 2pi units and T(n,m) is the complexity of a max-flow computation in a graph with n nodes and m edges. For nonconvex clique potentials, often used owing to their discontinuity preserving ability, we face an NP-hard problem for which we devise an approximate solution. Both algorithms solve integer optimization problems by computing a sequence of binary optimizations, each one solved by graph cut techniques. Accordingly, we name the two algorithms PUMA, for phase unwrapping max-flow/min-cut. A set of experimental results illustrates the effectiveness of the proposed approach and its competitiveness in comparison with state-of-the-art phase unwrapping algorithms