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Infrared and Color Visible Image Sequence Fusion Based on Statistical Model and Image Enhancement

机译:基于统计模型和图像增强的红外和彩色可见图像序列融合

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A novel fusion method is proposed for image sequence which based on the non-Gaussian statistical modeling of wavelet coefficients and image enhancement in IHS color space of color visible image. Firstly, the original color visible image is transformed into a perceptually decorrelated color space in order to treat the achromatic and chromatic components separately. Then, the achromatic component and infrared image are combined by a statistical fusion method based on dual tree complex wavelet transform (DT-CWT) using the generalized Gaussian distribution (GGD). The means and variances between the fused component and the original achromatic component are matched by a linear remapping for image enhancement. At last, the color space is transformed back into the RGB color space. This method not only have a superior fusion performance but also can effectively produce a high-contrast color fused image with the similar natural characteristics as the original color visible image.
机译:提出了一种新的融合方法,用于基于颜色可见图像的IHS颜色空间的小波系数和图像增强的非高斯统计建模的图像序列。首先,将原始颜色可见图像变换为感知的去相关颜色空间,以便分别地处理消色差和彩色组分。然后,使用广义高斯分布(GGD),通过基于双树复合小波变换(DT-CWT)的统计融合方法组合的消色差分量和红外图像。融合组件和原始消色组件之间的装置和差异通过用于图像增强的线性重新映射匹配。最后,将颜色空间转换回RGB颜色空间。这种方法不仅具有优异的融合性能,而且还可以有效地产生具有与原始颜色可见图像类似的自然特性的高对比度颜色融合图像。

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