Multi-focus image fusion approach based on CNP systems in NSCT domain

Coupled neural P (CNP) systems are recently developed distributed and parallel computing models that are abstracted by the mechanisms of coupled and spiking neurons. CNP systems differ from spiking neural P (SNP) systems in two main ways, namely the utilization of three data units, and a coupled firing and dynamic threshold mechanism for neurons. This paper focuses on the application of CNP systems to solve multi-focus image fusion problems, and proposes a novel image fusion approach based on CNP systems. Based on two CNP systems with local topology, a multi-focus image fusion framework in the non-subsampled contourlet transform (NSCT) domain is developed, where the two CNP systems are utilized to control the fusion of low-frequency coefficients in the NSCT domain. The proposed fusion approach is evaluated on an open data set of 19 multi focus images based on five fusion quality indices, and compared to 11 state-of-the-art fusion approaches. Quantitative and qualitative experimental results demonstrate the advantages of the proposed fusion approach in terms of visual quality and fusion performance.

Automated summary

This paper introduces the application of Coupled neural P (CNP) systems in solving multi-focus image fusion problems, proposing a novel image fusion approach based on CNP systems. Experimental results demonstrate the advantages of the proposed fusion approach in terms of visual quality and fusion performance.