一种基于频域的红外与可见光图像融合新算法

摘要

引入具有详细数学表达的多小波——V-系统,利用V-系统的多分辨性和NSCT的多方向性,对红外和可见光图像进行多层次、多方向分解,各层次各方向采用不同的融合方案进行图像融合.首先对源图像进行多层V-分解,得到图像的轮廓信息和多层细节信息;接着用NSCT对V分解得到的轮廓信息再分解,得到相应的低频和高频系数,低频系数用基于稀疏表示的融合规则融合,高频系数用基于二维Log-Gabor能量的融合规则融合,将改进的脉冲耦合神经网络融合规则用于V分解得到的细节信息的融合;最后,经过相应的逆变换得到融合图像.本文算法从不同层面、不同方向对源图像分解,使得源图像的细节得到细致刻画,同时多种融合方案的结合,使得融合图像的细节信息更加清晰,对比度得到提高,客观指标也有显著提高.%A multi-wavelet system with detailed mathematical expression called V-system is introduced.The infrared and visible images are decomposed into different layers and orientations by using multi-resolution of V-system and multi-orientation in non-subsampled contourlettransform(NSCT).And then different fusion strategies were adopted to fuse raw images in each layer and each orientation respectively.Firstly,the original image was decomposed by multilevel V-decomposition,and contour informations and multi-layer detailed informations of images were gotten;then the obtained contour informations were decompose again by NSCT to obtain low frequency and high frequency coefficients.The low frequency coefficients are fused according to the strategy based on sparse representation,and the high frequency coefficients are fused according to the strategy based on 2D Log-Gabor energy,and then the improved pulse coupled neural network was used to fuse multi-layer detailed information.Finally,the fused image is obtained by the corresponding inverse transformation.The algorithm decomposes images in different layers and orientations to obtain more refined detail of raw images.The combination of various fusion strategies makes the detailed information more clear and enhances the contrast of the fused images,and it also improves the objective indicators observably.