Heterogeneous Image Registration Algorithm Based on Fusion PC-ORB

WU Lang; YI Shi; CHEN Mengting; LI Li

Volume 46 Issue 4

Apr. 2024

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WU Lang, YI Shi, CHEN Mengting, LI Li. Heterogeneous Image Registration Algorithm Based on Fusion PC-ORB[J]. Infrared Technology , 2024, 46(4): 419-426.

Citation:

WU Lang, YI Shi, CHEN Mengting, LI Li. Heterogeneous Image Registration Algorithm Based on Fusion PC-ORB[J]. Infrared Technology , 2024, 46(4): 419-426.

WU Lang, YI Shi, CHEN Mengting, LI Li. Heterogeneous Image Registration Algorithm Based on Fusion PC-ORB[J]. Infrared Technology , 2024, 46(4): 419-426.

Citation:

WU Lang, YI Shi, CHEN Mengting, LI Li. Heterogeneous Image Registration Algorithm Based on Fusion PC-ORB[J]. Infrared Technology , 2024, 46(4): 419-426.

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Heterogeneous Image Registration Algorithm Based on Fusion PC-ORB

School of Mechanical and Electrical Engineering, Chengdu University of Technology, Chengdu 610059, China

Received Date: 2023-06-07
Rev Recd Date: 2023-07-11
Publish Date: 2024-04-20

Abstract

Abstract

In heterogeneous image registration, because of the differences in the imaging mechanisms, image pixel intensity correlation and rotation distortion are two inevitable problems. Aiming at the problem of image pixel intensity correlation, an image registration algorithm based on a radiation-invariant feature transform (RIFT) is proposed; it has good accuracy for image registration with small differences in the pixel correlation between images, but produces more error matching for rotation distortion images. For the problem of rotational distortion, the traditional Oriented Fast and Rotated Brief (ORB) algorithm has a certain degree of stability in the registration of rotating images; however, for image pairs with insignificant intensity changes, the quality of the feature point detection is low and the registration accuracy is not ideal. Therefore, this study integrates Phase Consistency into the ORB algorithm, replaces traditional image strength information with phase information, and constructs a rotation-invariant BRIEF feature descriptor that is robust to changes in the pixel strength and rotation distortion in the image. The registration experiment is conducted using infrared and visible-light images with unclear pixel intensity correlations. The algorithm proposed in this paper has high registration accuracy for images with different rotation amplitudes, and the RMSE is stable at 1.7−2.1, which is superior to the RIFT algorithm. It performs well in detecting a large number of feature points, achieving high registration accuracy, and maintaining efficiency.
- image registration,
- feature matching,
- phase consistency,
- rotational invariance,
- ORB algorithm

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References(16)

References

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