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Multi-focus Image Fusion Based on PCNN Model

机译:基于PCNN模型的多焦点图像融合

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摘要

Based on the PCNN model and contrast modulation method, a new multi-focus image fusion method is proposed in this paper. Send source images into the PCNN and compute the contrast. The characteristic of image region clustering enhances the veracity of contrast. Then using the normalization contrast modulation gets two fusion images. Finally, use local variance to get the new fusion image. The experiment indicates that the fusion image contains more information about the edge, texture and detail, and it has a better contrast. Compared with the common methods, the innovative method embodies better fusion performance in information, standard and average grads.
机译:基于PCNN模型和对比度调制方法,提出了一种新的多焦点图像融合方法。将源图像发送到PCNN并计算对比度。图像区域聚类的特征增强了对比度的准确性。然后使用归一化对比度调制获得两个融合图像。最后,使用局部方差获得新的融合图像。实验表明,融合图像包含有关边缘,纹理和细节的更多信息,并且具有更好的对比度。与常规方法相比,该创新方法在信息,标准和平均等级上体现出更好的融合性能。

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