Image fusion scheme using a novel dual-channel PCNN in lifting stationary wavelet domain
Image fusion scheme using a novel dual-channel PCNN in lifting stationary wavelet domain
复制标题
在提升平稳小波域中使用新型双通道 PCNN 的图像融合方案
DOI：
10.1016/j.optcom.2010.04.100
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发表时间：
2010-10-01
影响因子：
2.4
通讯作者：
Qu, J. F.
中科院分区：
文献类型：
--
作者：
Chai, Y.;Li, H. F.;Qu, J. F.
提出了一种基于提升平稳小波变换（LSWT）和双通道脉冲耦合神经网络（PCNN）的多源图像融合方法。通过使用LSWT，我们可以计算一个灵活的多尺度和平移不变的表示注册的图像。在对原始图像进行LSWT分解后，提出了一种新的双通道脉冲耦合神经网络，并将其用于LSWT子带系数的融合，克服了传统PCNN用于图像融合的一些缺点，直接输出融合图像。在该融合方案中，提出了一种新的低频子带图像的和-修正-拉普拉斯算子（NSML），它在SLWT域中表示低频子带图像的边缘特征，并将其输入到双通道PCNN中。对于高频子带系数的融合，提出了一种新的局部邻域修正拉普拉斯（LNML）测量，并将其作为外部激励来激励双通道PCNN。实验结果表明，与传统小波、LSWT和LSWT-PCNN等融合算法相比，本文提出的融合算法在客观准则和视觉效果上都有明显的提高。(C)2010 Elsevier B. V.保留所有权利。
This paper presents a new multi-source image fusion scheme based on lifting stationary wavelet transform (LSWT) and a novel dual-channel pulse-coupled neural network (PCNN). By using LSWT, we can calculate a flexible multiscale and shift-invariant representation of registered images. After decomposing the original images using LSWT, a new dual-channel pulse coupled neural network, which can overcome some shortcomings of original PCNN for image fusion and putout the fusion image directly, is proposed and used for the fusion of sub-band coefficients of LSWT. In this fusion scheme, a new sum-modified-laplacian(NSML) of the low frequency sub-band image, which represent the edge-feature of the low frequency sub-band image in SLWT domain, is presented and input to motivate the dual-channel PCNN. For the fusion of high frequency sub-band coefficients, a novel local neighborhood modified-laplacian (LNML) measurement is developed and used as external stimulus to motivate the dual-channel PCNN. This fusion scheme is verified on several sets of multi-source images, and the experiments show that the algorithms proposed in the paper can significantly improve image fusion performance, compared with the fusion algorithms such as traditional wavelet, LSWT, and LSWT-PCNN in terms of objective criteria and visual appearance. (C) 2010 Elsevier B.V. All rights reserved.