一种有效的基于并行量子进化算法的图像边缘检测方法

本文基于费用函数最小化方法,提出一种混合并行量子进化算法用于文本图像的边缘检测。量子进化算法是一种基于量予计算的概念和理论(诸如量子比特和量子叠加态)的进化算法,它采用了量子编码来表征染色体,由于量子比特的概率表示,能够表示出解的线性叠加状态。此外,量子进化算法具有收敛快和好的全局搜索特性,因此它比传统的进化算法更适于并行结构的实现。我们将这一算法和局部搜索算法相结合,用于图像的边缘检测问题,得到了令人满意的检测效果,并对噪声有较好的抑制作用。

An Effective Method of Image Edge Detection Based on Parallel Quantum Evolutionary Algorithm

In this paper we present a hybrid parallel quantum evolutionary algorithm (PQEA) based on cost minimization technique for edge detection. Quantum evolutionary algorithm (QEA) is based on the concept and principles of quantum computing such as qubits and superposition of states. By adopting qubit chromosome as a representation, QEA can represent a linear superposition of solutions due to its probabilistic representation. QEA is more suitable for parallel structure than the conventional evolutionary algorithms because of rapid convergence and good global search capability. We combine PQEA and the local search technique to the problem of edge detection. Experiment results show the algorithm perform very well in terms of quality of the final edge image, rate of convergence and robustness to noise.