基于非下采样Shearlet变换耦合相对亮度测度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 relative brightness measure

为了克服当前较多可见光与红外图像融合方法在通过图像能量特征融合图像时,忽略了图像的全局相对亮度特征,导致融合图像存在红外目标表达能力弱等问题,文章引入非下采样Shearlet变换,通过测量图像的相对亮度来融合图像。借助NSST变换来处理输入图像,解析出图像的低频和高频系数;采用区域能量函数,对区域图像所含的能量特征进行测量,利用图像的全局均值和区域均值,构造相对亮度测度模型,求取区域图像的相对亮度特征,并将区域图像的能量特征和相对亮度特征结合,对低频系数完成加权融合;利用图像的行、列、对角维度上的频率值,建立四维细节测量因子,以计算出图像的细节特征,完成高频系数融合,从而求取融合图像。实验结果表明,较当前算法的融合图像,所提算法不仅能更好的显示出图像的细节内容,而且还能更好的表达图像中的红外目标内容。

In order to overcome the problem that the global relative brightness feature of the image is ignored when the current visible and infrared image fusion methods fuse the image through the energy feature of the image,resulting in the weak expression ability of the infrared target in the fused image,a fusion method by measuring the relative brightness of the image based on the Nonsubsampled shearlet transform is designed.Firstly,the low-frequency and high-frequency coefficients of the image are analyzed by NSST transform.Then,the region energy function is used to measure the energy features contained in the region image.Based on the global mean value and regional mean value of the image,the relative brightness measure model is constructed to obtain the relative brightness feature of the regional image.The energy feature and relative brightness feature of regional image are combined to fuse the low frequency coefficients.Using the frequency values of the four dimensions of row,column and diagonal of the image,a four-dimensional detail measurement factor is established,which is used to calculate the fusion high-frequency coefficients of the image detail features,and then to obtain the fusion image.Experimental results show that the proposed algorithm can not only display the details of the image better,but also better express the content of the infrared target in the image.