基于各向异性引导滤波的红外与可见光图像融合
Infrared and Visible Image Fusion Using Anisotropic Guided Filtering
童朝阳 1杨莘 1杜世斌 1黄泽丰1
作者信息
- 1. 武汉科技大学信息科学与工程学院,湖北 武汉 430081
- 折叠
摘要
针对传统的红外与可见光图像多尺度融合方法中目标信息不够突出、细节丢失的问题,提出一种基于各向异性引导滤波的混合多尺度分解融合方法.首先,提出一种基于纹理轮廓的自适应图像增强方法对可见光图像进行增强,在增强纹理细节的同时提高图像暗区域的亮度和对比度.然后,利用各向异性引导滤波的边缘保持平滑特性提取源图像的亮度层,并结合高斯滤波将差异层分解为基础层、一级小尺度细节层和多级大尺度细节层.对亮度层采用绝对值取大的融合规则,对基础层提出一种视觉显著性结合最小二乘法优化的融合方法,对小尺度细节层采用修正拉普拉斯能量和融合策略,对大尺度细节层采用局部方差和空间频率的复合融合策略.最后,将融合后的各图层相加,重构融合图像.与其他9种经典和先进方法相比,所提方法在主观和客观分析上都有较好的表现.
Abstract
In response to concerns about the insufficient visibility of target information and loss of details in traditional multiscale fusion methods for infrared and visible images,this paper proposed a hybrid multiscale decomposition fusion method based on anisotropic guided filtering.Initially,an adaptive image enhancement method based on texture contours was introduced to improve visible images by simultaneously enhancing brightness,contrast in dark regions,and texture details.Subsequently,the brightness layer of the source image was extracted using the edge-preserving smoothing property of anisotropic guided filtering.The difference layer was decomposed into a base layer,a small-scale detail layer,and multiple levels of large-scale detail layers via Gaussian filtering.The fusion rule for the brightness layer employed an absolute maximum value approach,and a fusion method that combined visual saliency with least squares optimization was proposed for the base layer.The small-scale detail layer adopted a fusion strategy based on modified Laplacian energy,and the large-scale detail layers employed a composite fusion strategy based on local variance and spatial frequency.Finally,the fusion image was reconstructed by combining the merged layers.Compared with nine other classic and advanced methods,the proposed method performs well in both subjective and objective analyses.
关键词
红外与可见光图像/图像融合/各向异性引导滤波/混合多尺度分解Key words
infrared and visible image/image fusion/anisotropic guided filtering/hybrid multiscale decomposition引用本文复制引用
出版年
2024
NETL
NSTL
万方数据