Abstract
Pulse coupled neural network (PCNN) is used by predecessors to locate noise points, which overcomes the disadvantage that a large amount of image details will be lost in the traditional method of filtering the whole image. However, it is still a problem to find the optimal parameters of PCNN system. Therefore, the purpose of this paper is to improve the quality of image filtering and the flexibility of PCNN. This paper established a pulse coupled neural network model (PCNN-QSHA) based on quantum selfish herd algorithm. The PCNN’s optimal parameters can be obtained by the quantum selfish herd algorithm without manual estimation parameter. The experimental results show that, compared with the previous methods, the proposed method has excellent performance and efficiency in image filtering. By comparing the proposed algorithm with GACS, PSO and SFLA, nine CEC benchmark functions are simulated, and the results show that QSHA has better convergence performance.
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Gao, H., Zhao, H., Chen, S. (2022). Image Denoising Method of Auto-evolving PCNN Model Based on Quantum Selfish Herd Algorithm. In: Tan, Y., Shi, Y., Niu, B. (eds) Advances in Swarm Intelligence. ICSI 2022. Lecture Notes in Computer Science, vol 13344. Springer, Cham. https://doi.org/10.1007/978-3-031-09677-8_11
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DOI: https://doi.org/10.1007/978-3-031-09677-8_11
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