Dynamic threshold neural P systems (DTNP systems) are recently proposed distributed and parallel computing models, inspired from the intersecting cortical model. DTNP systems differ from spiking neural P systems (SNP systems) due to the introduction of dynamic threshold mechanism of neurons. DTNP systems have been theoretically proven to be Turing universal computing devices. This paper discusses how to apply DTNP systems to deal with the fusion of multi-focus images, and proposes a novel image fusion method based on DTNP systems in surfacelet domain. Based on four DTNP systems with local topology, a multi-focus image fusion framework in surfacelet domain is developed, where DTNP systems are applied to control the fusion of low- and high-frequency coefficients in surfacelet domain. The proposed fusion method is evaluated on an open dataset of 20 multi-focus images in terms of five fusion quality metrics, and compared with 10 state-of-the-art fusion methods. Quantitative and qualitative experimental results demonstrate the advantages of the proposed fusion method in terms of visual quality, fusion performance and computational efficiency.

动态阈值神经P系统（DTNP系统）是最近提出的分布式和并行计算模型，其灵感来自相交皮质模型。由于引入了神经元动态阈值机制，DTNP系统不同于尖峰神经P系统（SNP系统）。DTNP系统在理论上已被证明是图灵通用计算设备。本文讨论了如何应用DTNP系统来处理多焦点图像的融合，并提出了一种基于DTNP系统的小波域图像融合方法。基于四个具有局部拓扑结构的DTNP系统，开发了小波域中的多焦点图像融合框架，其中DTNP系统用于控制小波域中低频和高频系数的融合。根据五个融合质量指标，在包含20个多焦点图像的开放数据集上评估了提出的融合方法，并与10个最新融合方法进行了比较。定量和定性的实验结果证明了所提出的融合方法在视觉质量，融合性能和计算效率方面的优势。