A general memristor-based pulse coupled neural network with variable linking coefficient for multi-focus image fusion

Abstract

Pulse coupled neural network (PCNN) is a kind of visual cortex-inspired biological neural network, which has been proved a powerful candidate in the field of digital image processing due to its unique characteristics of global coupling and pulse synchronization. Notably, the inherent parameters estimation issue emerging in the entire system greatly affects the overall network performance. In this paper, a novel memristor crossbar array with its corresponding peripheral circuits is proposed, which is able to construct a general memristor-based PCNN (MPCNN) with variable linking coefficient. In order to verify the effectiveness and generality of the presented network, the single-channel MPCNN is further applied into the multi-focus image fusion problem with an improved multi-channel configuration. Correspondingly, a new type of MPCNN-based image fusion algorithm is put forward along with the design of an appropriate mapping function based on the image orientation information measure. Finally, a series of contrast experiments with comprehensive analysis demonstrate that the proposed fusion method has superior performances in terms of image quality and fusion effect compared to several existing algorithms.