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Tetrolet框架下红外与可见光图像融合

Fusion of Infrared and Visible Images Based on Tetrolet Framework

冯鑫  
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摘要

提出一种Tetrolet框架下基于联合稀疏表示结合改进脉冲耦合神经网络规则的红外与可见光图像融合方法.对源红外与可见光图像进行不考虑旋转和反射情况下的Tetrolet系数分解; 采用联合稀疏方法进行低频系数融合, 通过学习字典进行低频系数的精确拟合并融合.在高频子带系数融合上, 采用改进脉冲耦合神经网络设置相应的融合规则, 根据神经元的点火次数来选择融合图像的高频系数; 并对处理后的高低频系数值进行Tetrolet逆变换获取最终融合结果.结果表明, 该方法能够有效保留待融合图像的边缘与细节特征, 融合结果具有良好的视觉效果, 能够增强观察者对于场景的感知和重要目标的识别能力.在互信息、梯度信息、结构相似度以及视觉敏感度指标上都优于传统变换域融合方法, 尤其在结构相似度以及梯度保持度上分别领先0.033和0.025, 具有有效性.

Abstract

An fusion method based on joint sparse representation and improved pulse coupled neural network in tetrolet framework was proposed. The original infrared and visible images were decomposed without considering the rotation and reflection; for the low-frequency sub-band coefficients, the joint sparse representation method was used to accurately fit and fuse the low-frequency coefficients through the learning dictionary. In the high frequency subband coefficient fusion, the corresponding fusion rule was set by using the improved pulse coupled neural network, and the high frequency coefficient of the fused image was selected according to the number of firings of the neuron. The processed coefficient values were inversely transformed by tetrolet frame to obtain the final fusion result. The results show that the proposed method can effectively preserve the edge and detail features of the image to be fused, and the fusion results have better visual effects, which can enhance the observer′s ability to perceive the scene and identify important targets. It is superior to the traditional transform domain fusion method in mutual information, gradient information, structural similarity and visual sensitivity index, especially in terms of structural similarity and gradient retention, leading by 0.033 and 0.025, respectively, and has effectiveness.

Newport宣传-MKS新实验室计划
补充资料

中图分类号:TP391

DOI:10.3788/gzxb20194802.0210001

基金项目:国家自然科学基金(Nos.31501229,61861025),重庆市基础研究与前沿探索项目(Nos.csct2015jcyjA40014,cstc2018jcyjAX0483,cstc2015jcyja50027),重庆工商大学青年博士基金(No.1352007), 重庆工商大学博士启动基金(No.2014-56-07)

收稿日期:2018-08-14

修改稿日期:2018-11-30

网络出版日期:--

作者单位    点击查看

冯鑫:重庆工商大学 机械工程学院 制造装备机构设计与控制重庆市重点实验室, 重庆 400067

联系人作者:冯鑫(149495263@qq.com)

备注:冯鑫(1982-), 男, 副教授, 博士, 主要研究方向为智能信息处理、图像融合.

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引用该论文

FENG Xin. Fusion of Infrared and Visible Images Based on Tetrolet Framework[J]. ACTA PHOTONICA SINICA, 2019, 48(2): 0210001

冯鑫. Tetrolet框架下红外与可见光图像融合[J]. 光子学报, 2019, 48(2): 0210001

被引情况

【1】冯鑫,胡开群,袁毅,张建华,翟治芬. 基于超分辨率和组稀疏表示的多聚焦图像融合. 光子学报, 2019, 48(7): 710003--1

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