Open Access Open Access  Restricted Access Subscription or Fee Access

Extended Multi-channel Pulse Coupled Neural Network Model

Yaqian Zhao, Qinping Zhao, Aimin Hao

Abstract


Multi-channel Pulse coupled neural network (m-PCNN) is a recently proposed fusion model, which can finish the whole process of image fusion. Compared with single-channel PCNN based methods, m-PCNN is simple and fast, making it suitable to real-time system. But it is lack of control to feed function, which may result in false fired pulse. In this paper, we propose an extended m-PCNN model for improved image quality. This extended m-PCNN model not only adds linking strength parameters to adjust the impact of feed function, but also extends the adjust function for the fused result. Experimental results have shown the effectiveness of extended m-PCNN fusion model.

Keywords


pulse coupled neural network, image fusion, multi-channel PCNN

Full Text:

PDF

Refbacks

  • There are currently no refbacks.


Disclaimer/Regarding indexing issue:

We have provided the online access of all issues and papers to the indexing agencies (as given on journal web site). It’s depend on indexing agencies when, how and what manner they can index or not. Hence, we like to inform that on the basis of earlier indexing, we can’t predict the today or future indexing policy of third party (i.e. indexing agencies) as they have right to discontinue any journal at any time without prior information to the journal. So, please neither sends any question nor expects any answer from us on the behalf of third party i.e. indexing agencies.Hence, we will not issue any certificate or letter for indexing issue. Our role is just to provide the online access to them. So we do properly this and one can visit indexing agencies website to get the authentic information.