张健, 黄安穴. 基于改进LatLRR算法的红外与可见光图像融合研究[J]. 红外技术, 2024, 46(6): 672-680.
引用本文: 张健, 黄安穴. 基于改进LatLRR算法的红外与可见光图像融合研究[J]. 红外技术, 2024, 46(6): 672-680.
ZHANG Jian, HUANG Anxue. Infrared and Visible Image Fusion Based on Improved LatLRR Algorithm[J]. Infrared Technology , 2024, 46(6): 672-680.
Citation: ZHANG Jian, HUANG Anxue. Infrared and Visible Image Fusion Based on Improved LatLRR Algorithm[J]. Infrared Technology , 2024, 46(6): 672-680.

基于改进LatLRR算法的红外与可见光图像融合研究

Infrared and Visible Image Fusion Based on Improved LatLRR Algorithm

  • 摘要: 为了使红外与可见光图像融合后的目标图像更突出、背景信息更丰富,提出改进LatLRR算法。首先将潜在低秩分解获得的基础图层进行多级分解以便获得较多的基础图层、细节图层;接着多级分解控制,分别依据细节图层的水平分量、垂直分量的能量和基础图层的全局对比度,避免了无效分解;最后基础图层和细节图层融合采用不同的融合策略。实验仿真表明,本文算法融合结果具有层次感,图像比较清晰,纹理丰富,能够保持红外热辐射目标的轮廓细节信息,同时也保留大量的可见光图像背景特征,客观评价指标较优。

     

    Abstract: An improved latent low-rank representation(ILatLRR) is proposed to make the target more prominent and the background information more abundant after infrared and visible image fusion. First, the underlying layer obtained using LatLRR was decomposed at multiple levels to obtain additional underlying detail layers. Second, multilevel decomposition control was adopted based on the energy of the horizontal and vertical components of the detail layer and global contrast of the base layer to avoid invalid decomposition. Finally, different fusion strategies were adopted for the base and detail layers. The experimental simulations show that the fusion result of ILatLRR displays a sense of hierarchy; the image is clear; and the texture is rich. The contour details of the infrared thermal radiation target are maintained, retaining a large number of visible light image background features, with an objective evaluation index better than those of other algorithms.

     

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