A Novel Multi-focus Image Fusion Method using Pulse Coupled Neural Network in Nonsubsampled Contourlet Transform Domain

DOI

10.2991/iiicec-15.2015.60How to use a DOI?

Keywords

multi-focus image fusion; nonsubsampled contourlet transform; pulse coupled neural network; edge feature.

Abstract

In this paper, a novel image fusion method is proposed which combines nonsubsampled contourlet transform (NSCT) with PCNN. Firstly, it makes use of the NSCT’s shift invariance to suppress the pseudo-Gibbs phenomena around singularities, which appears in the DWT. Secondly, the edge feature is used to motive the improved PCNN model, to retain more edge and texture details. Some experiments are performed in images such as clock, pepsi and book images comparing the proposed algorithm with the SML-CT,PCNN-NSCT and SF-NSCT-PCNN methods The experimental results show that the proposed algorithm can not only extract more important visual information from source images, but also effectively avoid the introduction of artificial information.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Chaoben Du
AU  - Haifeng Yan
AU  - Shesheng Gao
AU  - Gaoge Hu
PY  - 2015/03
DA  - 2015/03
TI  - A Novel Multi-focus Image Fusion Method using Pulse Coupled Neural Network in Nonsubsampled Contourlet Transform Domain
BT  - Proceedings of the 2015 International Industrial Informatics and Computer Engineering Conference
PB  - Atlantis Press
SP  - 256
EP  - 261
SN  - 2352-538X
UR  - https://doi.org/10.2991/iiicec-15.2015.60
DO  - 10.2991/iiicec-15.2015.60
ID  - Du2015/03
ER  -