摘要
针对传统的红外与可见光图像融合出现的清晰度和对比度偏低,目标不够突出的问题,本文提出了一种基于Non-subsampledContourlet(NSCT)变换结合显著图与区域能量的融合方法。首先,使用改进的频率调谐(Frequency-tuned,FT)方法求出红外图像显著图并归一化得到显著图权重,单尺度Retinex(Single-scale Retinex,SSR)处理可见光图像。其次,使用NSCT分解红外与可见光图像,并基于归一化显著图与区域能量设计新的融和权重来指导低频系数融合,解决了区域能量自适应加权容易引入噪声的问题;采用改进的“加权拉普拉斯能量和”指导高频系数融合。最后,通过逆NSCT变换求出融合图像。本文方法与7种经典方法在6组图像中进行对比实验,在信息熵、互信息、平均梯度和标准差指标中最优,在空间频率中第一组图像为次优,其余图像均为最优结果。融合图像信息量丰富、清晰度高、对比度高并且亮度适中易于人眼观察,验证了本文方法的有效性。
To address the problems of low clarity and contrast of indistinct targets in traditional infrared and visible image-fusion algorithms,this study proposes a fusion method based on non-subsampled contourlet transform(NSCT)combined with a saliency map and region energy.First,an improved frequency-tuning(FT)method is used to obtain the infrared image saliency map,which is subsequently normalized to obtain the saliency map weight.A single-scale retinex(SSR)algorithm is then used to enhance the visible image.Second,NSCT is used to decompose the infrared and visible images,and a new fusion weight is designed based on the normalized saliency map and region energy to guide low-frequency coefficient fusion.This solves the problem of region-energy adaptive weighting being prone to introducing noise,and the improved"weighted Laplace energy sum"is used to guide the fusion of high-frequency coefficients.Finally,the fused image is obtained by inverse NSCT.Six groups of images were used to compare the proposed method with seven classical methods.The proposed method outperformed others in terms of information entropy,mutual information,average gradient,and standard deviation.Regarding spatial frequency,the first group of images was second best,and the remaining images exhibited the best results.The fusion images displayed rich information,high resolution,high contrast,and moderate brightness,demonstrating suitability for human observation,which verifies the effectiveness of this method.
作者
牛振华
邢延超
林英超
王晨轩
NIU Zhenhua;XING Yanchao;LIN Yingchao;WANG Chenxuan(School of Information and Control Engineering,Qingdao University of Technology,Qingdao 266520,China)
出处
《红外技术》
CSCD
北大核心
2024年第1期84-93,共10页
Infrared Technology
基金
山东省自然科学基金(ZR2021MF101)。
