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Remote Sensing
Volume 16
Issue 20
10.3390/rs16203804
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Article

Infrared and Visible Image Fusion via Sparse Representation and Guided Filtering in Laplacian Pyramid Domain

by
Liangliang Li
1,
Yan Shi
1,*,
Ming Lv
2,
Zhenhong Jia
2,
Minqin Liu
3,
Xiaobin Zhao
1,
Xueyu Zhang
1 and
Hongbing Ma
4
1
School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China
2
School of Computer Science and Technology, Xinjiang University, Urumqi 830046, China
3
National Key Laboratory of Space Integrated Information System, Institute of Software, Chinese Academy of Sciences, Beijing 100190, China
4
Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2024, 16(20), 3804; https://doi.org/10.3390/rs16203804
Submission received: 16 August 2024 / Revised: 21 September 2024 / Accepted: 27 September 2024 / Published: 13 October 2024
(This article belongs to the Special Issue Machine Learning for Intelligent Processing and Applications of Multi-Source Remote Sensing Data)
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Abstract

The fusion of infrared and visible images together can fully leverage the respective advantages of each, providing a more comprehensive and richer set of information. This is applicable in various fields such as military surveillance, night navigation, environmental monitoring, etc. In this paper, a novel infrared and visible image fusion method based on sparse representation and guided filtering in Laplacian pyramid (LP) domain is introduced. The source images are decomposed into low- and high-frequency bands by the LP, respectively. Sparse representation has achieved significant effectiveness in image fusion, and it is used to process the low-frequency band; the guided filtering has excellent edge-preserving effects and can effectively maintain the spatial continuity of the high-frequency band. Therefore, guided filtering combined with the weighted sum of eight-neighborhood-based modified Laplacian (WSEML) is used to process high-frequency bands. Finally, the inverse LP transform is used to reconstruct the fused image. We conducted simulation experiments on the publicly available TNO dataset to validate the superiority of our proposed algorithm in fusing infrared and visible images. Our algorithm preserves both the thermal radiation characteristics of the infrared image and the detailed features of the visible image.
Keywords: infrared and visible image; image fusion; Laplacian pyramid; sparse representation; guided filtering infrared and visible image; image fusion; Laplacian pyramid; sparse representation; guided filtering

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MDPI and ACS Style

Li, L.; Shi, Y.; Lv, M.; Jia, Z.; Liu, M.; Zhao, X.; Zhang, X.; Ma, H. Infrared and Visible Image Fusion via Sparse Representation and Guided Filtering in Laplacian Pyramid Domain. Remote Sens. 2024, 16, 3804. https://doi.org/10.3390/rs16203804

AMA Style

Li L, Shi Y, Lv M, Jia Z, Liu M, Zhao X, Zhang X, Ma H. Infrared and Visible Image Fusion via Sparse Representation and Guided Filtering in Laplacian Pyramid Domain. Remote Sensing. 2024; 16(20):3804. https://doi.org/10.3390/rs16203804

Chicago/Turabian Style

Li, Liangliang, Yan Shi, Ming Lv, Zhenhong Jia, Minqin Liu, Xiaobin Zhao, Xueyu Zhang, and Hongbing Ma. 2024. "Infrared and Visible Image Fusion via Sparse Representation and Guided Filtering in Laplacian Pyramid Domain" Remote Sensing 16, no. 20: 3804. https://doi.org/10.3390/rs16203804

APA Style

Li, L., Shi, Y., Lv, M., Jia, Z., Liu, M., Zhao, X., Zhang, X., & Ma, H. (2024). Infrared and Visible Image Fusion via Sparse Representation and Guided Filtering in Laplacian Pyramid Domain. Remote Sensing, 16(20), 3804. https://doi.org/10.3390/rs16203804

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