基于全局能量特征与改进PCNN的红外与可见光图像融合  

Infrared and Visible Image Fusion Based on Global Energy Features and Improved PCNN

作　　者：邢延超 牛振华 XING Yanchao;NIU Zhenhua(School of Information and Control Engineering,Qingdao University of Technology,Qingdao 266520,China)

机构地区：[1]青岛理工大学信息与控制工程学院,山东青岛266520

出　　处：《红外技术》2024年第8期902-911,共10页Infrared Technology

基　　金：山东省自然科学基金(ZR2021MF101)。

摘　　要：为了改善红外与可见光融合图像存在不清晰、图像对比度低以及缺少纹理细节的问题,本文提出了一种基于参数自适应脉冲耦合神经网络(parameter-adaptive pulse-coupled neural network,PAPCNN)图像融合算法。首先,对源红外图像进行暗通道去雾,增强图像的清晰度;然后,使用非下采样剪切波变换(non-subsampled shearlet transform,NSST)分解源图像,使用全局能量特征结合改进的空间频率自适应权重融合低频系数,将纹理能量作为PA-PCNN外部输入融合高频系数;最后,通过逆NSST变换得到最终融合灰度图像。本文方法与7种经典算法在2组图像中进行对比实验,实验结果表明:本文方法在评价指标中明显优于对比算法,提高了融合图像的清晰度和细节信息,验证了本文方法的有效性。将灰度图像转为伪彩色图像进一步增强了融合图像的辨识度和人眼的感知效果。To improve the low clarity,low contrast,and insufficient texture details of infrared and visible image fusion,an image fusion algorithm based on a parameter-adaptive pulse-coupled neural network(PA-PCNN)was proposed.First,the source infrared image was dehazed by a dark channel to enhance the clarity of the image.Then,the source images were decomposed by non-subsampled shearlet transform(NSST),and the low-frequency coefficients were fused by the proposed global energy feature extraction algorithm combined with a modified spatial frequency adaptive weight.Texture energy was used as the external input of the PA-PCNN to fuse the high-frequency coefficients,and the fused gray image was obtained using the inverse NSST.To further enhance the perception of the human eye,a multiresolution color transfer algorithm was used to convert the grayscale image to a color image.The proposed method was compared with seven classical algorithms for two image pairs.The experimental results show that the proposed method is significantly better than the comparison algorithms in terms of evaluation indicators,and improves the clarity and detail information of the fused image,which verifies its effectiveness.The conversion of the fused grayscale images into pseudo-color images further enhances recognition and human eye perception.

关 键 词：图像融合 非下采样剪切波变换 全局能量特征 纹理能量 脉冲耦合神经网络 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]