结合图像增强和多尺度分解的红外与可见光图像融合
Infrared and visible image fusion combining image enhancement and multiscale decomposition
罗宁 1修吉宏 2杨雪1
作者信息
- 1. 中国科学院长春光学精密机械与物理研究所航空光学成像与测量重点实验室,吉林长春 130033;中国科学院大学,北京 100049
- 2. 中国科学院长春光学精密机械与物理研究所航空光学成像与测量重点实验室,吉林长春 130033
- 折叠
摘要
针对近年来红外与可见光图像融合易发生细节信息丢失、对比度低等问题,提出一种结合可见光图像增强和多尺度分解的红外与可见光图像融合算法.首先,提出一种自适应可见光图像增强方法,提高了可见光图像的整体对比度,并增强了可见光图像中的细节信息.然后提出一种基于高斯滤波和滚动引导滤波的多尺度分解算法,将源图像分解为小尺度层、大尺度层和基础层,小尺度层融合采用基于最大绝对值的融合规则,大尺度层融合采用非线性权重系数将红外光谱特征注入到可见光图像中,基础层采用视觉显著映射的融合规则避免对比度损失,最后重建各尺度层生成融合图像.实验结果表明,与其他算法对比,所提方法的客观评价指标边缘保持度、基于人类启发感知的指标、空间频率、标准差、边缘强度分别平均提升了 23.50％、30.38％、46.67％、50.41％、20.17％、54.19％,此外,生成的融合图像在主观评价中也具有较好的表现.
Abstract
A fusion algorithm for infrared and visible light images combining visible light image enhancement and multiscale decomposition is proposed to address the problems of detail information loss and low contrast in infrared and visible light image fusion in recent years.Firstly,an adaptive visible light image enhancement method is proposed to improve the overall contrast of visible light images and enhance the detail information in visible light images.Then,a multiscale decomposition algorithm based on Gaussian filtering and rolling guided filtering is proposed to decompose the source image into small-scale layers,large-scale layers,and base layers.The small-scale layer fusion adopts a fusion rule based on maximum absolute value,the large-scale layer fusion injects the infrared spectral features into the visible light image using nonlinear weight coefficients,and the base layer adopts a fusion rule based on visual saliency mapping to avoid contrast loss.Finally,each scale layer is reconstructed to generate the fusion image.Experimental results show that compared with other algorithms,the proposed method has improved the objective evaluation indicators such as edge preservation,human-inspired perceptual metrics,spatial frequency,standard deviation,and edge intensity by an average of 23.50％,30.38％,46.67％,50.41％,20.17％,and 54.19％,respectively,and the generated fusion image also performs well in subjective evaluation.
关键词
图像增强/混合多尺度分解/滚动引导滤波/高斯滤波/图像融合Key words
image enhancement/hybrid multiscale decomposition/rolling guidance filter/Gaussian filter/image fusion引用本文复制引用
基金项目
国家高分重大专项(GFZX04030201)
出版年
2024
NETL
NSTL
万方数据