Multifocus Image Fusion Based on Nonsubsampled Contourlet Transform and Spiking Cortical Model

Title:

Multifocus Image Fusion Based on Nonsubsampled Contourlet Transform and Spiking Cortical Model

Creator:

Wang, Nianyi, Ma, Yide, Wang, Weilan, and Zhan, Kun

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:98eb173b-1d69-42c1-aca5-42d9d451c4ba
uuid:98eb173b-1d69-42c1-aca5-42d9d451c4ba
doi:10.14311/NNW.2015.25.031

Subject:

nonsubsampled contourlet transform (NSCT), spiking cortial model (SCM), multifocus image fusion, and pulse coupled neural network (PCNN)

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

A novel image fusion algorithm based on nonsubsampled contourlet transform (NSCT) and spiking cortical model (SCM) is proposed in this paper, aiming at solving the fusion problem of multifocus images. The fusion rules of subband coefficients of NSCT are discussed, and a new maximum selection rule (MSR) is defined to fuse low frequency coefficients instead of using traditional MSR directly. For the fusion rule of high frequency coefficients, spatial frequency (SF) of each high frequency subband is considered as the gradient features of images to motivate SCM networks and generate pulse of neurons, and then the time matrix of SCM is set as criteria to select coefficients of high frequency subband. Experimental results and visual evaluation demonstrate the effectiveness of the proposed fusion method. Objective tests and analysis conducted under different noised source image environments proved the robustness of the proposed fusion method.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2015 Volume:25 | Number:6

Harvested from:

CDK

Metadata only:

false