Coarse-to-Fine Homography Estimation for Infrared and Visible Images

被引:3
|
作者
Wang, Xingyi [1 ]
Luo, Yinhui [1 ]
Fu, Qiang [1 ]
He, Yuanqing [1 ]
Shu, Chang [1 ]
Wu, Yuezhou [1 ]
Liao, Yanhao [1 ]
机构
[1] Civil Aviat Flight Univ China, Sch Comp Sci, Guanghan 618307, Peoples R China
关键词
homography estimation; coarse-to-fine; infrared image; visible image;
D O I
10.3390/electronics12214441
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Homography estimation for infrared and visible images is a critical and fundamental task in multimodal image processing. Recently, the coarse-to-fine strategy has been gradually applied to the homography estimation task and has proved to be effective. However, current coarse-to-fine homography estimation methods typically require the introduction of additional neural networks to acquire multi-scale feature maps and the design of complex homography matrix fusion strategies. In this paper, we propose a new unsupervised homography estimation method for infrared and visible images. First, we design a novel coarse-to-fine strategy. This strategy utilizes different stages in the regression network to obtain multi-scale feature maps, enabling the progressive refinement of the homography matrix. Second, we design a local correlation transformer (LCTrans), which aims to capture the intrinsic connections between local features more precisely, thus highlighting the features crucial for homography estimation. Finally, we design an average feature correlation loss (AFCL) to enhance the robustness of the model. Through extensive experiments, we validated the effectiveness of all the proposed components. Experimental results demonstrate that our method outperforms existing methods on synthetic benchmark datasets in both qualitative and quantitative comparisons.
引用
收藏
页数:20
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