红外技术2019，Vol.41Issue(2)：176-182,7.
基于BLMD和NSDFB算法的红外与可见光图像融合方法
Infrared and Visible Image Fusion Based on BLMD and NSDFB
摘要
Abstract
Because traditional image fusion methods can easily cause blurred details and dim targets, a new fusion approach based on bidimensional local mean decomposition (BLMD) and nonsubsampled directional filter banks (NSDFBs) for visible–infrared images is proposed. In this fusion framework, the entropies of two source images are first calculated, and the residue of the image whose entropy is larger is extracted, which is highly relevant for the other source images. Then, the residue and the other source image are decomposed into low-frequency subbands and a sequence of high-frequency directional subbands in different scales by using BLMD and NSDFBs. At the fusion stage, two relevant fusion rules are used in low-frequency subbands and high-frequency directional subbands, respectively. Finally, the fused image is obtained by applying the corresponding inverse transform. Experimental results show that the proposed fusion algorithm can obtain state-of-the-art performance for visible–infrared fusion images in the aspects of both objective assessment and subjective visual quality, even when the source images are captured in different conditions.Furthermore, the fused results have higher contrast, richer details, and more-remarkable targets than those of Laplacian image fusion methods, increasing by 5.6％, 28.9％, 37.4％, and 47.6％ in the information entropy (IE) , standard deviation (SD) , spatial frequency (SF) and average gradient (AG) , respectively, while decreasing by 8.5％ in peak signal-to-noise ratio.关键词
图像融合/二维局部均值分解/非下采样方向滤波器组/残余分量Key words
image fusion/BLMD/NSDFB/residue分类
信息技术与安全科学引用本文复制引用
周晨旭,黄福珍..基于BLMD和NSDFB算法的红外与可见光图像融合方法[J].红外技术,2019,41(2):176-182,7.基金项目
上海市电站自动化技术重点实验室资助项目 (13DZ2273800) （13DZ2273800）
