An Infrared and Visible Fusion Framework Based on a Novel Decomposition Method

Xiao, Rui; Cheng, Feiyan; Shi, Junsheng; Wang, Yuanfangzhou; Li, Chengli

doi:10.3390/sym14040786

Cited by 2 publications

(1 citation statement)

References 43 publications

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“…One of the most popular topics in vision is multimodal image registration; in particular, infrared and visible image registration is the most studied [3,4]. Since infrared and visible images can provide complementary information, they have wide applications in image fusion [5,6], automatic transform detection [7], and super-resolution reconstruction [8].…”

Section: Introductionmentioning

confidence: 99%

RI-MFM: A Novel Infrared and Visible Image Registration with Rotation Invariance and Multilevel Feature Matching

Zhu

Zhan

et al. 2022

Electronics

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In the past ten years, multimodal image registration technology has been continuously developed, and a large number of researchers have paid attention to the problem of infrared and visible image registration. Due to the differences in grayscale distribution, resolution and viewpoint between two images, most of the existing infrared and visible image registration methods are still insufficient in accuracy. To solve such problems, we propose a new robust and accurate infrared and visible image registration method. For the purpose of generating more robust feature descriptors, we propose to generate feature descriptors using a concentric-circle-based feature-description algorithm. The method enhances the description of the main direction of feature points by introducing centroids, and, at the same time, uses concentric circles to ensure the rotation invariance of feature descriptors. To match feature points quickly and accurately, we propose a multi-level feature-matching algorithm using improved offset consistency for matching feature points. We redesigned the matching algorithm based on the offset consistency principle. The comparison experiments with several other state-of-the-art registration methods in CVC and homemade datasets show that our proposed method has significant advantages in both feature-point localization accuracy and correct matching rate.

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