红外与可见光融合图像的质量评价

王宇庆; 王索建

doi:10.3788/CO.20140703.0396

红外与可见光融合图像的质量评价

doi: 10.3788/CO.20140703.0396

王宇庆^1, ,,
王索建²

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中国科学院重点实验室, 吉林长春 130033;
2.
南京电子技术研究所, 江苏南京 210039

基金项目:

中国博士后科学基金资助项目（No.20080441004）

详细信息

作者简介:
王宇庆（1979- ），男，吉林长春人，博士后，助理研究员，2002年、2005年于吉林大学分别获得学士、硕士学位，2008年于中国科学院长春光学精密机械与物理研究所获得博士学位，2008~2010年在中国科学院长春光学精密机械与物理研究所机械工程博士后流动站工作，主要从事图像质量评价、图像增强、图像融合、FPGA设计、群体智能方面的研究。E-mail：wyq7903@aliyun.com

通讯作者:
王宇庆

中图分类号: TP391.4
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- 被引次数: 0
出版历程
- 收稿日期: 2013-12-14
- 修回日期: 2014-02-16
- 刊出日期: 2014-05-25

Quality assessment method of IR and visible fusion image

WANG Yu-qing^{1
, ,},
WANG Suo-jian²

1.
Key Laboratory of Airborne Optical Imaging and Measurement, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China;
2.
Nanjing Institute of Electronics Technology, Nanjing 210039, China

摘要

摘要: 为了提高融合图像质量评价中评价结果与人眼视觉特性的一致性，分析了现有融合图像质量评价方法，提出了一种基于图像结构信息复数表示的融合图像质量评价方法，通过计算图像亮度分量的梯度，构成了一种表征图像结构信息的梯度复数矩阵，用该矩阵表征图像的结构信息。考虑到复数无法计算互信息等参数，将分块奇异值分解后得到的矩阵作为度量矩阵，采用该矩阵计算了两种融合图像质量评价方法。实验结果表明，该方法提高了评价结果与人眼视觉特性的一致性，对于融合效果较好的金字塔和小波方法给出了3.748 5和3.722 2的评价结果，与人眼视觉特性的一致性优于传统方法。
- 像质评价 /
- 图像融合 /
- 梯度 /
- 复数
Abstract: In order to improve the consistency of the assessment result of image fusion with that of Human Visual System, the state-of-the-art image fusion assessment methods are deeply analysed, then a new assessment method is proposed in this paper, which is based on the complex number expression for image structure. The gradient information of luminance layer of color image is used to perform the task. When it is used to describe image structure, more human visual system-sensitive information are contain in the corresponding complex matrix. Due to the calculation problem of mutual information, we perform singular value decomposition on the complex matrix, and the singular value vector of each image block is used to construct the new matrix. Results from experiments show that the proposed method gives evaluation of 3.748 5 and 3.722 2 for pyramid and DWT methods. It improves the consistency of assessment results with those of human visual system.
- image quality assessment /
- image fusion /
- gradient /
- complex number

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