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IR and Visible Images Registration method based on Cross Cumulative Residual Entropy

机译:基于交叉累积残差熵的红外与可见图像配准方法

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This paper presents a method which combines with Bilateral Filter and cross cumulative residual entropy. It will be applied to infrared and visible registration. In this algorithm, firstly, according to infrared image and optical image characteristics, we put forward edge extraction algorithm based on the Bilateral Filter. Secondly, we use Cross Cumulative Residual Entropy (CCRE) as the similarity measure to match the reference images and transformed images effectively. Finally, we introduce the idea of calibration to reduce operation time. Bilateral filter can reduce noise and protect edge, and cross cumulative residual entropy uses cumulative distribution function instead of probability density function to overcome the noise on the local minima. The experiment proved that registration is effective.
机译:本文提出了一种结合双边滤波和交叉累积残差熵的方法。它将应用于红外和可见配准。在该算法中,首先根据红外图像和光学图像的特点,提出了一种基于双边滤波器的边缘提取算法。其次,我们使用交叉累积残差熵(CCRE)作为相似性度量来有效匹配参考图像和变换图像。最后,我们介绍了校准的想法,以减少操作时间。双边滤波器可以减少噪声并保护边缘,交叉累积残差熵使用累积分布函数而不是概率密度函数来克服局部极小值上的噪声。实验证明注册是有效的。

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