基于非下采样Shearlet变换与剥离策略的可见光与红外图像融合算法

The fusion algorithm of visible and infrared image based on nonsubsampled shearlet transform coupled with peeling strategy

为了使可见光与红外融合图像能更好的表达目标信息,联合非下采样Shearlet变换与剥离策略,对其进行融合。首先,借助非下采样Shearlet变换,获取可见光与红外图像的高、低频成分。然后,通过Otsu阈值分割方法来构建剥离策略,将红外图像中的目标层与其背景层进行剥离,并以目标层为基础,利用图像的区域能量特征,对可见光图像与红外图像背景层的低频系数进行加权计算,并将结果与红外图像的目标层结合,以得到富含目标内容和背景内容的融合低频系数。引入区域方差函数,对图像的细节特征进行测算,通过构造方差加权因子,得到富含细节特征的融合高频系数。最后,对两个融合系数实施非下采样Shearlet逆变换,从而输出融合图像。实验结果显示,本算法融合的图像,比现有融合算法融合的图像更具优良的目标及细节表达能力,可用于获取高质量的可见光与红外融合图像。

In order to make the fusion image of visible and infrared better express the target information, the non-subsampled Shearlet transform and split strategy are combined to fuse them. First of all, the high and low frequency components of visible and infrared images are obtained by using the non-sampling shearlet transform. Then, the Otsu thresholding segmentation method is used to develop the stripping strategy for ping the target layer and its background layer of infrared image. Based on the target layer, the low-frequency coefficients of the background layer of the visible image and the infrared image are weighted by using the regional energy characteristics of the image, and the result is combined with the target layer of the infrared image to obtain the fusion low-frequency coefficients which are rich in the target content and background content.The regional variance function is introduced to measure the detail features of the image,and by constructing the variance weighting factor,the fusion high-frequency coefficient which riched in detail features is obtained.Finally,the non-subsampled Shearlet inverse transform is applied to the two fusion coefficients for outputting the fused image.The experimental results show that the fusion image of this algorithm has better target and detail expression ability than the existing fusion image,which can be used to obtain high-quality visible and infrared fusion image.