Polarized image registration method based on phase correlation and sub-graph

Image registration is a very fundamental and important part in many multi-sensor image based applications. Phase correlation-based image registration method is widely concerned for its small computation amount, strong anti-interference property. However, it can only solve the image registration problem with translational motion. Hence, we proposed a modified phase correlation registration method in the paper. We analyzed the principle of registration, gave the flow chart, and applied the method to the SAR image registration problems with scaling, rotation and translation transformation. Simulation results show that the method can accurately estimate the translation parameters, zoom scale and rotation angle of registrating image relative to the reference image.

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A Novel Coarse-to-Fine Scheme for Remote Sensing Image Registration Based on SIFT and Phase Correlation

Remote Sensing ◽

10.3390/rs11151833 ◽

2019 ◽

Vol 11 (15) ◽

pp. 1833 ◽

Cited By ~ 5

Author(s):

Han Yang ◽

Xiaorun Li ◽

Liaoying Zhao ◽

Shuhan Chen

Keyword(s):

Remote Sensing ◽

Image Registration ◽

Phase Correlation ◽

Mean Square ◽

Extended Phase ◽

Registration Method ◽

Coarse Registration ◽

Feature Based ◽

Coarse To Fine ◽

Fine Registration

Automatic image registration has been wildly used in remote sensing applications. However, the feature-based registration method is sometimes inaccurate and unstable for images with large scale difference, grayscale and texture differences. In this manuscript, a coarse-to-fine registration scheme is proposed, which combines the advantage of feature-based registration and phase correlation-based registration. The scheme consists of four steps. First, feature-based registration method is adopted for coarse registration. A geometrical outlier removal method is applied to improve the accuracy of coarse registration, which uses geometric similarities of inliers. Then, the sensed image is modified through the coarse registration result under affine deformation model. After that, the modified sensed image is registered to the reference image by extended phase correlation. Lastly, the final registration results are calculated by the fusion of the coarse registration and the fine registration. High universality of feature-based registration and high accuracy of extended phase correlation-based registration are both preserved in the proposed method. Experimental results of several different remote sensing images, which come from several published image registration papers, demonstrate the high robustness and accuracy of the proposed method. The evaluation contains root mean square error (RMSE), Laplace mean square error (LMSE) and red–green image registration results.

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An Extension of Phase Correlation-Based Image Registration to Estimate Similarity Transform Using Multiple Polar Fourier Transform

Remote Sensing ◽

10.3390/rs10111719 ◽

2018 ◽

Vol 10 (11) ◽

pp. 1719 ◽

Cited By ~ 8

Author(s):

Yunyun Dong ◽

Weili Jiao ◽

Tengfei Long ◽

Guojin He ◽

Chengjuan Gong

Keyword(s):

Fourier Transform ◽

Image Registration ◽

Fractional Fourier Transform ◽

Correlation Method ◽

High Accuracy ◽

Phase Correlation ◽

Estimation Methods ◽

Registration Method ◽

Similarity Transform ◽

Polar Grid

Image registration is a core technology of many different image processing areas and is widely used in the remote sensing community. The accuracy of image registration largely determines the effect of subsequent applications. In recent years, phase correlation-based image registration has drawn much attention because of its high accuracy and efficiency as well as its robustness to gray difference and even slight changes in content. Many researchers have reported that the phase correlation method can acquire a sub-pixel accuracy of 1 / 10 or even 1 / 100 . However, its performance is acquired only in the case of translation, which limits the scope of the application of the method. However, there are few reports on the estimation of scales and angles based on the phase correlation method. To take advantage of the high accuracy property and other merits of phase correlation-based image registration and extend it to estimate the similarity transform, we proposed a novel algorithm, the Multilayer Polar Fourier Transform (MPFT), which uses a fast and accurate polar Fourier transform with different scaling factors to calculate the log-polar Fourier transform. The structure of the polar grids of MPFT is more similar to the one of the log-polar grid. In particular, for rotation estimation only, the polar grid of MPFT is the calculation grid. To validate its effectiveness and high accuracy in estimating angles and scales, both qualitative and quantitative experiments were carried out. The quantitative experiments included a numerical simulation as well as synthetic and real data experiments. The experimental results showed that the proposed method, MPFT, performs better than the existing phase correlation-based similarity transform estimation methods, the Pseudo-polar Fourier Transform (PPFT) and the Multilayer Fractional Fourier Transform method (MLFFT), and the classical feature-based registration method, Scale-Invariant Feature Transform (SIFT), and its variant, ms-SIFT.

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Robust image registration using subspace method in Radon transform domain

Sensor Review ◽

10.1108/sr-10-2018-0277 ◽

2019 ◽

Vol 39 (5) ◽

pp. 645-651

Author(s):

Ning Wei ◽

Yu He ◽

Junqing Liu ◽

Peng Chen

Keyword(s):

Image Registration ◽

Radon Transform ◽

Median Filter ◽

Least Square ◽

Phase Correlation ◽

Transform Domain ◽

Registration Method ◽

Content Type ◽

Structural Differences ◽

Robust Image

Purpose The purpose of this paper is to represent a robust image registration method to align noisy and deformed images in their Radon transform domain. Due to the limitation of imaging mechanism, the images are often highly noisy. Even worse, the objects in images have structural differences from time to time. Design/methodology/approach To eliminate these degressions, the proposed method is equipped with subspace-based power spectrum analysis algorithm for rotation estimation and a new global median filter least square algorithm for displacement computation. Findings Experiments on strongly noisy and degenerated images show that the proposed method exhibits better accuracy and robustness than phase correlation-based method. In addition, the method can also be applied to multi-modal registration, where the results are comparable to mutual information method but spending much less time. Originality/value A robust image registration method is proposed, which has better performance than traditional methods.

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Image Registration Method Based On Edge Phase Correlation Algorithm

Proceedings of the 2016 3rd International Conference on Materials Engineering, Manufacturing Technology and Control ◽

10.2991/icmemtc-16.2016.304 ◽

2016 ◽

Author(s):

Qing Zhou ◽

Cheng Yang ◽

Lingyun Wan ◽

Ying Zhang ◽

Xiaolong Zhou ◽

...

Keyword(s):

Image Registration ◽

Phase Correlation ◽

Registration Method ◽

Correlation Algorithm

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Phase Correlation Method for ITK

The Insight Journal ◽

10.54294/qerh5x ◽

2006 ◽

Author(s):

Jakub Bican

Keyword(s):

Fourier Transform ◽

Image Registration ◽

Matched Filter ◽

Correlation Method ◽

Phase Correlation ◽

Initial Image ◽

Registration Method ◽

Complex Image ◽

Overlap Area ◽

Symmetric Phase

Phase Correlation Method (PCM or SPOMF - Symmetric Phase-Only Matched Filter) is a well known image registration method, that exploits Fourier Shift Theorem property of Fourier Transform. Even though it is limited to estimate only shifts between two images, it is very useful as it is robust to frequency-dependent noise and initial image overlap area. Furthermore, it evaluates very fast by way of two forward FFTs, one complex image division and one inverse FFT.

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The Extension of Phase Correlation to Image Perspective Distortions Based on Particle Swarm Optimization

Sensors ◽

10.3390/s19143117 ◽

2019 ◽

Vol 19 (14) ◽

pp. 3117

Author(s):

Xue Wan ◽

Chenhui Wang ◽

Shengyang Li

Keyword(s):

Particle Swarm Optimization ◽

Image Registration ◽

Particle Swarm ◽

Motion Blur ◽

Phase Correlation ◽

Geometric Transformation ◽

Reference Image ◽

Swarm Optimization ◽

Registration Method ◽

Geometric Distortions

Phase correlation is one of the widely used image registration method in medical image processing and remote sensing. One of the main limitations of the phase correlation-based registration method is that it can only cope with Euclidean transformations, such as translation, rotation and scale, which constrain its application in wider fields, such as multi-view image matching, image-based navigation, etc. In this paper, we extended the phase correlation to perspective transformation by the combination of particle swarm optimization. Inspired by optic lens alignment based on interference, we propose to use the quality of PC fringes as the similarity, and then the aim of registration is to search for the optimized geometric transformation operator, which obtain the maximize value of PC-based similarity function through particle swarm optimization approach. The proposed method is validated by image registration experiments using simulated terrain shading, texture and natural landscape images containing different challenges, including illumination variation, lack of texture, motion blur, occlusion and geometric distortions. Further, image-based navigation experiments are carried out to demonstrate that the proposed method is able to correctly recover the trajectory of camera using multimodal target and reference image. Even under great radiometric and geometric distortions, the proposed method is able to achieve 0.1 sub-pixel matching accuracy on average while other methods fail to find the correspondence.

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