NSST框架下结合局部自适应亮度与DCAPCNN的双波段图像融合  

Infrared and visible image fusion based on NSST framework combining local adaptive intensity and dual channel adaptive pulse coupled neural network

作　　者：姜迈 郑岩[1,3] 李宏达 张晓顺[2,3] 于遨洋[2,3] JIANG Mai;ZHENG Yan;LI Hongda;ZHANG Xiaoshun;YU Aoyang(Criminal Investigation and Counter-Terrorism College,Criminal Investigation Police University of China,Shenyang,Liaoning 110854,China;School of Forensic Science and Technology,Criminal Investigation Police University of China,Shenyang,Liaoning 110854,China;Key Laboratory of Impression Evidence Examination and Identification Technology,Ministry of Public Security of People's Republic of China,Shenyang,Liaoning 110854,China)

机构地区：[1]中国刑事警察学院侦查与反恐怖学院,辽宁沈阳110854 [2]中国刑事警察学院刑事科学技术学院,辽宁沈阳110854 [3]痕迹检验鉴定技术公安部重点实验室,辽宁沈阳110854

出　　处：《光电子．激光》2023年第9期932-941,共10页Journal of Optoelectronics·Laser

基　　金：中央高校基本科研项目(20230823);公安部科技强警基础工作专项项目(2019GABJC06);公安部技术研究计划(2020JS YJC26,2019JSYJC23);公安理论及软科学研究计划(2019LLYJXJXY055,2019LLYJXJXY057);辽宁省自然基金指导计划(2020-MS-131)资助项目。

摘　　要：针对现有红外与可见光图像融合后,易出现边缘平滑严重、纹理细节恢复不足、对比度低、显著目标不突出、部分信息缺失等问题,提出一种基于非下采样剪切波变换(non-subsampled shearlet transform,NSST)的红外与可见光双波段图像融合算法。首先,采用基于自适应引导滤波(adaptive guided filter,AGF)的方法对源红外、可见光图像增强。其次,利用NSST正变换分别对源红外与可见光图像分解,得到红外、可见光图像的低、高频子带分量。然后,分别通过基于局部自适应亮度(local adaptive intensity,LAI)与双通道自适应脉冲耦合神经网络(dual channel adaptive pulse coupled neural network,DCAPCNN)规则融合低、高频子带分量。最后,通过NSST逆变换得到最终融合图像。实验结果表明,本文算法整体对比度更适宜,对红外热目标及可见光背景的边缘与纹理的细节恢复性更好,融合图像信噪比高,有效结合了红外及可见光图像的各自优势,与现有传统图像融合与深度学习融合算法相比,本文算法达到了更好的实验效果,在主观视觉感知和客观指标评价中均具有更好的融合性能。In order to solve the traditional image fusion deficiencies,such as excessive edge smoothing,loss texture details,low contrast,non-prominent target and missing source image information,this paper proposes an infrared and visible dual-band image fusion algorithm based on non-subsampled shearlet transform(NSST).Firstly,the source infrared and visible images are enhanced through adaptive guided filter(AGF).Secondly,the infrared and visible images are decomposed into low and high frequency components by NSST,respectively.Then,the low frequency components are fused by using the local adaptive intensity(LAI)rule,while high frequency components are fused by using dual channel adaptive pulse coupled neural network(DCAPCNN).Finally,the fused image is reconstructed by using the inverse NSST.Experimental results show that the proposed method has advantages in appropriate contrast,reserving the infrared target characteristic,including more background edge and texture detail information,and fusion image with high signal-noise ratio,the infrared and visible image advantage are effectively combined,compared with the existing traditional and deep learning fusion algorithms,the proposed algorithm achieves better experimental results,with superior performance in both subjective visual perception and objective indicator evaluations.

关 键 词：图像融合 非下采样剪切波变换(NSST) 自适应引导滤波(AGF) 局部自适应亮度(LAI) 双通道自适应脉冲耦合神经网络(DCAPCNN) 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]