摘要
枝切法是一种高效的抗噪声相位展开方法,而最短枝切长度能够保证最优的相位展开结果.最短枝切长度问题属于组合优化问题,提出一种求解该问题的学习算法,将最短枝切长度问题的解视为个体,该算法通过个体之间的学习以及个体自身的变异实现进化,作用类似于遗传算法中的交叉算子以及变异算子.通过对多幅含噪声包裹相位图进行实验验证,该算法比传统的求解最短枝切长度问题的算法更快更优.
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.
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2011年第5期645-650,共6页
Pattern Recognition and Artificial Intelligence
基金
国家自然科学基金(No.60775025)
江苏省自然科学基金(BK2010058)资助项目
关键词
最短枝切长度问题
相位展开
组合优化
学习算法
Shortest Branch Cut Length Problem, Phase Unwrapping, Combinatorial Optimization,Learning Algorithm
