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Multi-focus Image Fusion Based on The Nonsubsampled Contourlet Transform and Dual-layer PCNN Model

机译：基于非下采样Contourlet变换和双层PCNN模型的多焦点图像融合

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摘要

Image fusion is an important research field of image processing . How to get the best fusion quality is not an easy problem for the researchers. The nonsubsampled contourlet transform (NSCT) is a multi-resolution tool for image fusion. For NSCT, how to get a better focus measurement is an important research content. In this study, a new model named dual-layer PCNN model is proposed. It simulates human visual perception mechanism. The model not only takes into account local neighbor relativity each other but also takes into account the relativity between before and after layers. Compared with the other PCNN models, this model uses the Shannon information entropy to adaptively control its iteration process. Based on this model and NSCT, a new image fusion method is proposed. In the method, the source images are decomposed by NSCT firstly and then the dual-layer PCNN model and local energy match rule are used to select the coefficients. At last, the fused image is reconstructed by taking an inverse NSCT. The experimental results show that the dual-layer PCNN model is a good focus measurement for NSCT and the method proposed in this study has better fusion performance than the other classical methods.
机译：图像融合是图像处理的重要研究领域。对于研究人员而言，如何获得最佳的融合质量并非易事。非下采样轮廓波变换（NSCT）是用于图像融合的多分辨率工具。对于NSCT，如何获得更好的焦点测量是重要的研究内容。在这项研究中，提出了一种称为双层PCNN模型的新模型。它模拟了人类的视觉感知机制。该模型不仅考虑了彼此的本地邻居相对性，而且考虑了前后各层之间的相对性。与其他PCNN模型相比，该模型使用Shannon信息熵来自适应地控制其迭代过程。基于该模型和NSCT，提出了一种新的图像融合方法。该方法首先通过NSCT分解源图像，然后使用双层PCNN模型和局部能量匹配规则来选择系数。最后，通过逆NSCT重建融合图像。实验结果表明，双层PCNN模型是NSCT的良好焦点测量方法，与其他经典方法相比，本文提出的方法具有更好的融合性能。

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