Coupled neural P (CNP) systems are a recently developed Turing-universal, distributed and parallel computing model, combining the spiking and coupled mechanisms of neurons. This paper focuses on how to apply CNP systems to handle the fusion of multi-modality medical images and proposes a novel image fusion method. Based on two CNP systems with local topology, an image fusion framework in nonsubsampled shearlet transform (NSST) domain is designed, where the two CNP systems are used to control the fusion of low-frequency NSST coefficients. The proposed fusion method is evaluated on 20 pairs of multi-modality medical images and compared with seven previous fusion methods and two deep-learning-based fusion methods. Quantitative and qualitative experimental results demonstrate the advantage of the proposed fusion method in terms of visual quality and fusion performance.

中文翻译：

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非下采样Shearlet变换域中基于耦合神经P系统的医学图像融合方法

耦合神经 P (CNP) 系统是最近开发的图灵通用、分布式和并行计算模型，结合了神经元的尖峰和耦合机制。本文重点关注如何应用 CNP 系统处理多模态医学图像的融合，并提出一种新的图像融合方法。基于两个具有局部拓扑结构的CNP系统，设计了一个非下采样剪切波变换（NSST）域的图像融合框架，两个CNP系统用于控制低频NSST系数的融合。所提出的融合方法在 20 对多模态医学图像上进行了评估，并与之前的 7 种融合方法和两种基于深度学习的融合方法进行了比较。