Illumination-Robust Subpixel Fourier-Based Image Correlation Methods Based on Phase Congruency

Ye, Zhen; Tong, Xiaohua; Zheng, Shouzhu; Guo, Chengcheng; Gao, Su; Liu, Shijie; Jin, Yanmin; Xie, Huan; Chen, Peng

doi:10.1109/tgrs.2018.2870422

Cited by 31 publications

(16 citation statements)

References 60 publications

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“…Frequency-based method matches images based on translation model or similarity model using the information and operation in frequency domain [25]. This method is highly efficient and robust to the frequency-dependent noise and intensity contrast [11,12].…”

Section: Phase Correlation-based Image Registration Methodsmentioning

confidence: 99%

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

Yang

Zhao

et al. 2019

Remote Sensing

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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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Section: Phase Correlation-based Image Registration Methodsmentioning

confidence: 99%

“…(x 2 , y 2 ) are obtained through the real affine transformation model as calculated by manual selection. In Equations (24) and (25), x i,j is the grayscale value of the point (i, j) in the reference image, and x * i,j is the point in the registered image.…”

Section: Evaluation Criteriamentioning

confidence: 99%

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

Yang

Zhao

et al. 2019

Remote Sensing

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“…The optimization using ZNSSD is much easier. PC is also a commonly used patch-based similarity measure that conducts normalization in the frequency domain based on the translation property of Fourier transform [53,54]. Different from NCC, the computation of PC avoids the iterative search process due to the use of Fourier transform routine.…”

Section: Similarity Measuresmentioning

confidence: 99%

Area-Based Dense Image Matching with Subpixel Accuracy for Remote Sensing Applications: Practical Analysis and Comparative Study

Xu²,

Chen

et al. 2020

Remote Sensing

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Dense image matching is a crucial step in many image processing tasks. Subpixel accuracy and fractional measurement are commonly pursued, considering the image resolution and application requirement, especially in the field of remote sensing. In this study, we conducted a practical analysis and comparative study on area-based dense image matching with subpixel accuracy for remote sensing applications, with a specific focus on the subpixel capability and robustness. Twelve representative matching algorithms with two types of correlation-based similarity measures and seven types of subpixel methods were selected. The existing matching algorithms were compared and evaluated in a simulated experiment using synthetic image pairs with varying amounts of aliasing and two real applications of attitude jitter detection and disparity estimation. The experimental results indicate that there are two types of systematic errors: displacement-dependent errors, depending on the fractional values of displacement, and displacement-independent errors represented as unexpected wave artifacts in this study. In addition, the strengths and limitations of different matching algorithms on the robustness to these two types of systematic errors were investigated and discussed.

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“…Phase correlation is adopted as the basic matching method in this study, which is a Fourier-based matching technique and is considered to be more accurate and effective than the commonly used area-based methods such as normalized cross-correlation (Ye et al, 2019). Phase correlation is based on the well-known Fourier shift property, which states that a shift of two relevant images in the spatial domain is transformed into the Fourier domain as linear phase differences.…”

Section: Pixel-level Disparity Estimationmentioning

confidence: 99%

Precise Disparity Estimation for Narrow Baseline Stereo Based on Multiscale Superpixels and Phase Correlation

Hoegner

et al. 2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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<p><strong>Abstract.</strong> With the rapid development of subpixel matching algorithms, the estimation of image shifts with an accuracy of higher than 0.05 pixels is achieved, which makes the narrow baseline stereovision possible. Based on the subpixel matching algorithm using the robust phase correlation (PC), in this work, we present a novel hierarchical and adaptive disparity estimation scheme for narrow baseline stereo, which consists of three main steps: image coregistration, pixel-level disparity estimation, and subpixel refinement. The Fourier-Mellin transform with subpixel PC is used to co-register two input images. Then, the pixel-level disparities are estimated in an iterative manner, which is achieved through multiscale superpixels. The pixel-level PC is performed with the window sizes and locations adaptively determined according to superpixels, with the disparity values calcualted. Fast weighted median filtering based on edge-aware filter is adopted to refine the disparity results. At last, the accurate disparities are calculated via a robust subpixel PC method. The combination of multiscale superpixel hierarchy, adaptive determination of the window size and location of correlation, fast weighted median filtering and subpixel PC make the proposed scheme be able to overcome the issues of either low-texture areas or fattening effect. Experimental results on a pair of UAV images and the comparison with the fixed-window PC methods, the iterative scheme with fixed variation strategy, and a sophisticated implementation using global optimization demonstrate the superiority and reliability of the proposed scheme.</p>

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Illumination-Robust Subpixel Fourier-Based Image Correlation Methods Based on Phase Congruency

Cited by 31 publications

References 60 publications

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

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

Area-Based Dense Image Matching with Subpixel Accuracy for Remote Sensing Applications: Practical Analysis and Comparative Study

Precise Disparity Estimation for Narrow Baseline Stereo Based on Multiscale Superpixels and Phase Correlation

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