NSST框架下结合局部自适应亮度与DCAPCNN的双波段图像融合

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。

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

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.