Abstract：Branch cut method is an effcient noise-immune algorithm for correct phase unwrapping of noisy phase maps. The shortest branch cut length promises the optimal unwrapping of the wrapped phase maps. The shortest branch cut length problem belongs to combinatorial optimizations. A learning algorithm is proposed to resolve the problem. One solution for the problem is one individual for the algorithm. Individuals learn from other individuals and mutate by themselves to realize the evolution, which is similar to the crossover and mutation operator in the genetic algorithm. Compared with the traditional methods, the learning algorithm is fast and competitive.
[1] Salfity M F, Ruiz P D, Huntley J M, et al. Branch Cut Surface Placement for Unwrapping of Undersampled Three-Dimensional Phase Data: Application to Magnetic Resonance Imaging Arterial Flow Mapping. Applied Optics, 2006, 45(12): 2711-2722 [2] Goldstein R M, Zebker H A, Werner C L. Satellite Radar Interferometry: Two-Dimensional Phase Unwrapping. Radio Science, 1988, 23(4): 713-720 [3] Huntley J M, Saldner H O. Temporal Phase-Unwrapping Algorithm for Automated Interferogram Analysis. Applied Optics, 1993, 32(17): 3047-3052 [4] Gutmann B, Weber H. Phase Unwrapping with the Branch-Cut Method: Role of Phase-Field Direction. Applied Optics, 2000, 39(26): 4802-4816 [5] Cusack R, Huntley J M, Goldrein H T. Improved Noise-Immune Phase-Unwrapping Algorithm. Applied Optics, 1995, 34(5): 781-789 [6] Buckland J R, Huntley J M, Turner J M. Unwrapping Noisy Phase Maps by Use of a Minimum-Cost-Matching Algorithm. Applied Optics, 1995, 34(23): 5100-5108 [7] Karout S A, Gdeisat M A, Burton D R, et al. Two-Dimensional Phase Unwrapping Using a Hybrid Genetic Algorithm. Applied Optics, 2007, 46(5): 730-743 [8] Gutmann B. Phase Unwrapping with the Branch-Cut Method: Clustering of Discontinuity Sources and Reverse Simulated Annealing. Applied Optics, 1999, 38(26): 5577-5793 [9] Carretero J A, Nahon M A. Solving Minimum Distance Problems with Convex or Concave Bodies Using Combinatorial Global Optimization Algorithms. IEEE Trans on Systems, Man and Cybernetics, 2005, 35(6): 1144-1153 [10] Baraglia R, Hidalgo J I, Perego R. A Hybrid Heuristic for the Traveling Salesman Problem. IEEE Trans on Evolutionary Computation, 2001, 5(6): 613-622 [11] Zheng Dongliang, Xue Yuncan, Yang Qiwen, et al. Modified Discrete Particle Swarm Optimization Algorithm Based on Inver-Over Operator. Pattern Recognition and Artificial Intelligence, 2010, 23(1): 97-102 (in Chinese) (郑东亮,薛云灿,杨启文,等.基于Inver-Over 算子的改进离散粒子群优化算法.模式识别与人工智能, 2010, 23(1): 97-102)
2011, Vol. 24 
