Intelligent matching method for visible and infrared images based on style translation

Qingge Li; Xiaogang Yang; Ruitao Lu; Siyu Wang; Jiwei Fan; Hai Xia

doi:10.1117/12.3000011

18 December 2023 Intelligent matching method for visible and infrared images based on style translation

Qingge Li, Xiaogang Yang, Ruitao Lu, Siyu Wang, Jiwei Fan, Hai Xia

Proceedings Volume 12960, AOPC 2023: Infrared Devices and Infrared Technology; and Terahertz Technology and Applications; 1296004 (2023) https://doi.org/10.1117/12.3000011
Event: Applied Optics and Photonics China 2023 (AOPC2023), 2023, Beijing, China

Abstract

Heterogeneous image matching is a hot and difficult research topic in the field of image processing. The existing visible and infrared image matching has problems such as large modal differences, difficult matching, and poor robustness. Therefore, an intelligent matching method for visible and infrared images based on BCE-CycleGAN is proposed. First, a BCECycleGAN model is proposed based on the image style translation with generative adversarial networks, it can convert visible images to infrared images. By designing a new generative network loss function, the transformation effect of the model on heterogeneous images is improved. Then, the generated infrared images are matched with the original infrared images using LoFTR and DFM algorithms. LoFTR and DFM are currently advanced deep learning-based intelligent matching algorithms. Finally, the conversion relationship is mapped to the corresponding visible and infrared image pair to obtain the final matching result. Images style translation experiments and matching experiments on the test datasets show that the BCE-CycleGAN network proposed in this paper can effectively reduce the complexity of the algorithm and improve the quality of image generation. Furthermore, combining BCE-CycleGAN with deep learning-based matching methods can effectively improve the effectiveness and robustness of the matching algorithm.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Qingge Li, Xiaogang Yang, Ruitao Lu, Siyu Wang, Jiwei Fan, and Hai Xia "Intelligent matching method for visible and infrared images based on style translation", Proc. SPIE 12960, AOPC 2023: Infrared Devices and Infrared Technology; and Terahertz Technology and Applications, 1296004 (18 December 2023); https://doi.org/10.1117/12.3000011

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
11 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

IRIS Consortium

Iris

Infrared imaging

Infrared radiation

Design for manufacturing

Visible radiation

Education and training

Show All Keywords

Keywords/Phrases

Search In:

Publication Years