基于混合模型驱动的红外与可见光图像融合
作者:
作者单位:

兰州交通大学电子与信息工程学院

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中图分类号:

TP391

基金项目:

国家自然科学基金(批准号:61861025,61562057,61761027,51669010)资助的课题。教育部长江学者和创新团队发展计划(批准号:IRT_16R36)资助的课题。光电技术与智能控制教育部重点实验室(兰州交通大学)开放课题(批准号:KFKT2018-9)、兰州市人才创新创业项目(批准号:2018-RC-117)资助的课题。


Infrared and Visible Image Fusion Based on Hybrid Model Driving
Author:
Affiliation:

School of Electronic and Information Engineering, Lanzhou Jiaotong University

Fund Project:

Project supported by the National Natural Science Foundation of China (Grant Nos. 61861025,61562057,61761027,51669010) . Project supported by the Program for Changjiang Scholars and Innovative Research Team in University of Ministry of Education of China (Grant No. IRT_16R36). Project supported by Key Laboratory of Opto-technology and Intelligent Control, Ministry of Education(Lanzhou Jiaotong University) Open topic (Grant No. KFKT2018-9). Project supported by Lanzhou Talent Innovation and Entrepreneurship Project (Grant No. 2018-RC-117).

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    摘要:

    为了解决红外与可见光图像融合中显著特征不突出、图像对比度低的问题,提出一种混合模型驱动的融合算法。首先,采用潜在低秩表示模型分别提取红外与可见光图像的基础子带、显著子带及稀疏噪声子带;然后,采用非下采样剪切波变换模型将基础子带分解为低频系数和高频系数,对低频系数采用字典学习与稀疏表示进行精确拟合,高频系数采用局部窗口结合逻辑加权进行选择;显著子带采用区域能量比阈值自适应加权法进行融合;最后,对融合后的低频系数和高频系数进行一级重建,得到融合基础子带,舍弃稀疏噪声子带,结合融合显著子带进行二级重建得到融合图像。实验结果表明,该算法能够得到蕴含丰富信息且较为清晰的融合图像,具有可行性;融合结果的对比度较高、目标轮廓显著,能够提升场景的辨识度,具有有效性。

    Abstract:

    In order to solve the problem of unprominent features and low image contrast in infrared and visible image fusion, a hybrid model-driven fusion algorithm is proposed. First, the latent low-rank representation model is used to extract the base subband, significant subband and sparse noise subband of infrared and visible images respectively. Then, the base subbands are decomposed into low frequency coefficient and high frequency coefficient by using the non-subsampled shearlet transform modle, and the low frequency coefficient is accurately fitted by dictionary learning and sparse representation, while the high frequency coefficient is selected by local window combined with logic weighting. The significant subbands were fused by adaptive weighting of regional energy ratio threshold. Finally, the low frequency coefficient and high frequency coefficient after fusion were reconstructed in the first order to obtain the fusion base subband, while the sparse noise subband was discarded, and the fusion image was obtained by combining the fusion significant subband with the second-order reconstruction. The experimental results show that this algorithm can obtain the fusion image with rich information and relatively clear, which is feasible. The fusion result has high contrast and significant target contour, which can improve the recognition of the scene and has validity.

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  • 收稿日期:2019-12-14
  • 最后修改日期:2021-03-30
  • 录用日期:2020-05-29
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