Image fusion algorithm based on orientation information motivated Pulse Coupled Neural Networks
Image fusion algorithm based on orientation information motivated Pulse Coupled Neural Networks
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DOI：
10.1109/wcica.2008.4593305
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发表时间：
2008-06
期刊：
影响因子：
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通讯作者：
X. Qu;Changwei Hu;Jingwen Yan
中科院分区：
文献类型：
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作者：
X. Qu;Changwei Hu;Jingwen Yan
脉冲耦合神经网络（PCNN）是一种受视觉皮层启发的神经网络，具有神经元间全局耦合和脉冲同步的特点。该方法已被证明适用于图像处理，并成功地应用于图像融合。然而，在大多数基于PCNN的融合算法中，仅输入单个像素值来激励PCNN神经元。这不够有效，因为人类通常对特征敏感，而不仅仅是像素值。在本文中，新的方向信息被认为是特征，以激励PCNN。视觉观察和客观性能评价标准表明，该算法优于典型的基于小波，基于Lapacian金字塔变换和基于PCNN的融合算法。
Pulse Coupled Neural Networks (PCNN) is a visual cortex-inspired neural networks and characterized by the global coupling and pulse synchronization of neurons. It has been proven suitable for image processing and successfully employed in image fusion. However, in most PCNN-based fusion algorithms, only single pixel value is input to motivate PCNN neuron. This is not effective enough because humans are often sensitive to features, not only pixel value. In this paper, novel orientation information is considered as features to motivate PCNN. Visual observation and objective performance evaluation criteria demonstrate that the proposed algorithm outperforms typical wavelet-based, lapacian pyramid transform-based and PCNN-based fusion algorithms.