scholarly journals A Fast Subpixel Registration Algorithm Based on Single-Step DFT Combined with Phase Correlation Constraint in Multimodality Brain Image

2020 ◽  
Vol 2020 ◽  
pp. 1-10 ◽  
Author(s):  
Jianguo Li ◽  
Quanhai Ma
Keyword(s):  
Fourier Transform ◽  
Image Registration ◽  
Cross Correlation ◽  
Matrix Multiplication ◽  
Single Step ◽  
Phase Correlation ◽  
Brain Image ◽  
Registration Algorithm ◽  
Bidirectional Search ◽  
Coarse Registration

Multimodality brain image registration technology is the key technology to determine the accuracy and speed of brain diagnosis and treatment. In order to achieve high-precision image registration, a fast subpixel registration algorithm based on single-step DFT combined with phase correlation constraint in multimodality brain image was proposed in this paper. Firstly, the coarse positioning at the pixel level was achieved by using the downsampling cross-correlation model, which reduced the Fourier transform dimension of the cross-correlation matrix and the multiplication of the discrete Fourier transform matrix, so as to speed up the coarse registration process. Then, the improved DFT multiplier of the matrix multiplication was used in the neighborhood of the coarse point, and the subpixel fast location was achieved by the bidirectional search strategy. Qualitative and quantitative simulation experiment results show that, compared with comparison registration algorithms, our proposed algorithm could greatly reduce space and time complexity without losing accuracy.

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

2019 ◽  
Vol 11 (15) ◽  
pp. 1833 ◽  
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.

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

2018 ◽  
Vol 10 (11) ◽  
pp. 1719 ◽  
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.

scholarly journals Eliminating the Effect of Image Border with Image Periodic Decomposition for Phase Correlation Based Remote Sensing Image Registration

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2329 ◽  
Author(s):  
Yunyun Dong ◽  
Weili Jiao ◽  
Tengfei Long ◽  
Lanfa Liu ◽  
Guojin He
Keyword(s):  
Remote Sensing ◽  
Fourier Transform ◽  
Image Registration ◽  
Success Rate ◽  
High Efficiency ◽  
High Energy ◽  
Phase Correlation ◽  
Window Function ◽  
Registration Accuracy ◽  
Strong Discontinuities

In the remote sensing community, accurate image registration is the prerequisite of the subsequent application of remote sensing images. Phase correlation based image registration has drawn extensive attention due to its high accuracy and high efficiency. However, when the Discrete Fourier Transform (DFT) of an image is computed, the image is implicitly assumed to be periodic. In practical application, it is impossible to meet the periodic condition that opposite borders of an image are alike, and image always shows strong discontinuities across the frame border. The discontinuities cause a severe artifact in the Fourier Transform, namely the known cross structure composed of high energy coefficients along the axes. Here, this phenomenon was referred to as effect of image border. Even worse, the effect of image border corrupted its registration accuracy and success rate. Currently, the main solution is blurring out the border of the image by weighting window function on the reference and sensed image. However, the approach also inevitably filters out non-border information of an image. The existing understanding is that the design of window function should filter as little information as possible, which can improve the registration success rate and accuracy of methods based on phase correlation. In this paper, another approach of eliminating the effect of image border is proposed, namely decomposing the image into two images: one being the periodic image and the other the smooth image. Replacing the original image by the periodic one does not suffer from the effect on the image border when applying Fourier Transform. The smooth image is analogous to an error image, which has little information except at the border. Extensive experiments were carried out and showed that the novel algorithm of eliminating the image border can improve the success rate and accuracy of phase correlation based image registration in some certain cases. Additionally, we obtained a new understanding of the role of window function in eliminating the effect of image border, which is helpful for researchers to select the optimal method of eliminating the effect of image border to improve the registration success rate and accuracy.

scholarly journals Spectral biometrical recognition of fingerprints

2011 ◽  
Vol 1 (2) ◽  
Author(s):  
Tibor Csongrády ◽  
Elena Pivarčiová
Keyword(s):  
Fourier Transform ◽  
Image Registration ◽  
Access Control ◽  
Control Systems ◽  
Mellin Transform ◽  
Phase Correlation ◽  
Fingerprint Recognition ◽  
Fingerprint Verification ◽  
The Fourier Transform ◽  
Fourier Mellin Transform

AbstractThe article is aimed at fingerprint recognition or verification, which is used mainly in access control systems. The proposed method of fingerprint verification uses image registration (reconciliation) by applying the Fourier-Mellin transform and consequently compares images by the Fourier transform using modified phase correlation.

scholarly journals Estimation of Large Scalings in Images Based on Multilayer Pseudopolar Fractional Fourier Transform

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Zhenhong Li ◽  
Jianwei Yang ◽  
Ming Li ◽  
Rushi Lan
Keyword(s):  
Fourier Transform ◽  
Image Registration ◽  
Motion Estimation ◽  
Fractional Fourier Transform ◽  
Phase Correlation ◽  
Polar Coordinates ◽  
Accurate Estimation ◽  
Registration Accuracy ◽  
Polar Grid ◽  
The Fourier Transform

Accurate estimation of the Fourier transform in log-polar coordinates is a major challenge for phase-correlation based motion estimation. To acquire better image registration accuracy, a method is proposed to estimate the log-polar coordinates coefficients using multilayer pseudopolar fractional Fourier transform (MPFFT). The MPFFT approach encompasses pseudopolar and multilayer techniques and provides a grid which is geometrically similar to the log-polar grid. At low coordinates coefficients the multilayer pseudopolar grid is dense, and at high coordinates coefficients the grid is sparse. As a result, large scalings in images can be estimated, and better image registration accuracy can be achieved. Experimental results demonstrate the effectiveness of the presented method.

scholarly journals Phase Correlation Method for ITK

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.

An image registration algorithm based on phase correlation and the classical Lucas–Kanade technique

2017 ◽  
Vol 11 (7) ◽  
pp. 1321-1328 ◽  
Author(s):  
Youssef Douini ◽  
Jamal Riffi ◽  
Adnane Mohamed Mahraz ◽  
Hamid Tairi
Keyword(s):  
Image Registration ◽  
Phase Correlation ◽  
Registration Algorithm ◽  
Image Registration Algorithm
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