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

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

Mathematical Problems in Engineering ◽

10.1155/2013/179489 ◽

2013 ◽

Vol 2013 ◽

pp. 1-9 ◽

Cited By ~ 4

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.

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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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A Modified Phase Correlation Method for Image Registration

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.48-49.48 ◽

2011 ◽

Vol 48-49 ◽

pp. 48-51

Author(s):

Lu Jing Yang ◽

Wei Hao ◽

Chong Lun Li

Keyword(s):

Image Registration ◽

Rotation Angle ◽

Translational Motion ◽

Correlation Method ◽

Phase Correlation ◽

Flow Chart ◽

Reference Image ◽

Sar Image ◽

Registration Method ◽

Simulation Results

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

Chinese Journal of Liquid Crystals and Displays ◽

10.3788/yjyxs20193405.0530 ◽

2019 ◽

Vol 34 (5) ◽

pp. 530-536

Author(s):

万钇良 WAN Yi-liang ◽

王建立 WANG Jian-li ◽

张楠 ZHANG Nan ◽

姚凯男 YAO Kai-nan ◽

王昊京 WANG Hao-jing

Keyword(s):

Image Registration ◽

Phase Correlation ◽

Registration Method

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Nyquist Zone Index and Chirp Rate Estimation of LFM Signal Intercepted by Nyquist Folding Receiver Based on Random Sample Consensus and Fractional Fourier Transform

Sensors ◽

10.3390/s19061477 ◽

2019 ◽

Vol 19 (6) ◽

pp. 1477 ◽

Cited By ~ 1

Author(s):

Xinqun Liu ◽

Tao Li ◽

Xiaolei Fan ◽

Zengping Chen

Keyword(s):

Fourier Transform ◽

Random Sample ◽

Fractional Fourier Transform ◽

Estimation Method ◽

Ultra Wideband ◽

Estimation Methods ◽

Radar Signal ◽

Chirp Rate ◽

Time Frequency ◽

Random Sample Consensus

The Nyquist folding receiver (NYFR) can achieve a high-probability interception of an ultra-wideband (UWB) signal with fewer devices, while the output of the NYFR is converted into a hybrid modulated signal of the local oscillator (LO) and the received signal, which requires the matching parameter estimation methods. The linear frequency modulation (LFM) signal is a typical low probability of intercept (LPI) radar signal. In this paper, an estimation method of both the Nyquist Zone (NZ) index and the chirp rate for the LFM signal intercepted by NYFR was proposed. First, according to the time-frequency characteristics of the LFM signal, the accurate NZ and the rough chirp rate was estimated based on least squares (LS) and random sample consensus (RANSAC). Then, the information of the LO was removed from the hybrid modulated signal by the known NZ, and the precise chirp rate was obtained by using the fractional Fourier transform (FrFT). Moreover, a fast search method of FrFT optimal order was presented, which could obviously reduce the computational complexity. The simulation demonstrated that the proposed method could precisely estimate the parameters of the hybrid modulated output signal of the NYFR.

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Remote Sensing Image Registration Based on Spatial Transform Network and Phase Correlation Method

2019 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS) ◽

10.1109/iciibms46890.2019.8991540 ◽

2019 ◽

Author(s):

Chen Ying ◽

Chen Hengshi ◽

Liu Guoqing

Keyword(s):

Remote Sensing ◽

Image Registration ◽

Correlation Method ◽

Remote Sensing Image ◽

Phase Correlation

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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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