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| A Learning Algorithm for Shortest Branch Cut Length Problem |
| ZHENG Dong-Liang, DA Fei-Peng |
| School of Automation, Southeast University, Nanjing 210096 |
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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.
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Received: 19 July 2010
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2011, Vol. 24 
