A Registration Method of Fundus Images Based on Edge Detection and Phase-Correlation

2006 ◽  
Author(s):  
Wei Wang ◽  
Houjin Chen ◽  
Jupeng Li ◽  
Jiangbo Yu
Keyword(s):  
Edge Detection ◽  
Phase Correlation ◽  
Fundus Images ◽  
Registration Method

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

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

A Modified Phase Correlation Method for Image Registration

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.

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 FAST AND ROBUST REGISTRATION OF AERIAL IMAGES AND LIDAR DATA BASED ON STRUCTURAL FEATURES AND 3D PHASE CORRELATION

2020 ◽  
Vol V-2-2020 ◽  
pp. 135-141
Author(s):  
B. Zhu ◽  
Y. Ye ◽  
C. Yang ◽  
L. Zhou ◽  
H. Liu ◽  
...  
Keyword(s):  
Structural Features ◽  
Phase Correlation ◽  
Aerial Images ◽  
Aerial Image ◽  
Reference Image ◽  
Feature Descriptor ◽  
Lidar Data ◽  
Three Dimension ◽  
Interest Points ◽  
Registration Method

Abstract. Co-Registration of aerial imagery and Light Detection and Ranging (LiDAR) data is quilt challenging because the different imaging mechanism causes significant geometric and radiometric distortions between such data. To tackle the problem, this paper proposes an automatic registration method based on structural features and three-dimension (3D) phase correlation. In the proposed method, the LiDAR point cloud data is first transformed into the intensity map, which is used as the reference image. Then, we employ the Fast operator to extract uniformly distributed interest points in the aerial image by a partition strategy and perform a local geometric correction by using the collinearity equation to eliminate scale and rotation difference between images. Subsequently, a robust structural feature descriptor is build based on dense gradient features, and the 3D phase correlation is used to detect control points (CPs) between aerial images and LiDAR data in the frequency domain, where the image matching is accelerated by the 3D Fast Fourier Transform (FFT). Finally, the obtained CPs are employed to correct the exterior orientation elements, which is used to achieve co-registration of aerial images and LiDAR data. Experiments with two datasets of aerial images and LiDAR data show that the proposed method is much faster and more robust than state of the art methods.

Robust image registration using subspace method in Radon transform domain

Sensor Review ◽  
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.

Optic disc detection in retinal fundus images using gravitational law-based edge detection

2016 ◽  
Vol 55 (6) ◽  
pp. 935-948 ◽  
Author(s):  
Mohammad Alshayeji ◽  
Suood Abdulaziz Al-Roomi ◽  
Sa’ed Abed
Keyword(s):  
Edge Detection ◽  
Optic Disc ◽  
Fundus Images ◽  
Retinal Fundus Images ◽  
Retinal Fundus

scholarly journals Improving Co-Registration for Sentinel-1 SAR and Sentinel-2 Optical Images

2021 ◽  
Vol 13 (5) ◽  
pp. 928
Author(s):  
Yuanxin Ye ◽  
Chao Yang ◽  
Bai Zhu ◽  
Liang Zhou ◽  
Youquan He ◽  
...  
Keyword(s):  
European Space Agency ◽  
Phase Correlation ◽  
Optical Data ◽  
Geometric Transformation ◽  
Interest Points ◽  
Registration Accuracy ◽  
Registration Method ◽  
Sensing Applications ◽  
Optical Images ◽  
Sentinel 2

Co-registering the Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 optical data of the European Space Agency (ESA) is of great importance for many remote sensing applications. However, we find that there are evident misregistration shifts between the Sentinel-1 SAR and Sentinel-2 optical images that are directly downloaded from the official website. To address that, this paper presents a fast and effective registration method for the two types of images. In the proposed method, a block-based scheme is first designed to extract evenly distributed interest points. Then, the correspondences are detected by using the similarity of structural features between the SAR and optical images, where the three-dimensional (3D) phase correlation (PC) is used as the similarity measure for accelerating image matching. Lastly, the obtained correspondences are employed to measure the misregistration shifts between the images. Moreover, to eliminate the misregistration, we use some representative geometric transformation models such as polynomial models, projective models, and rational function models for the co-registration of the two types of images, and we compare and analyze their registration accuracy under different numbers of control points and different terrains. Six pairs of the Sentinel-1 SAR L1 and Sentinel-2 optical L1C images covering three different terrains are tested in our experiments. Experimental results show that the proposed method can achieve precise correspondences between the images, and the third-order polynomial achieves the most satisfactory registration results. Its registration accuracy of the flat areas is less than 1.0 10 m pixel, that of the hilly areas is about 1.5 10 m pixels, and that of the mountainous areas is between 1.7 and 2.3 10 m pixels, which significantly improves the co-registration accuracy of the Sentinel-1 SAR and Sentinel-2 optical images.

scholarly journals ROBUST REGISTRATION FOR OPTICAL AND SAR IMAGES BASED ON SPATIAL GEOMETRIC CONSTRAINT AND STRUCTURE FEATURES

2020 ◽  
Vol V-1-2020 ◽  
pp. 117-124
Author(s):  
M. Wang ◽  
Y. Ye ◽  
M. Sun ◽  
X. Tan ◽  
L. Li
Keyword(s):  
Template Matching ◽  
Three Dimensional ◽  
Phase Correlation ◽  
Geometric Constraint ◽  
Feature Descriptor ◽  
Automatic Registration ◽  
Geometric Correction ◽  
Registration Accuracy ◽  
Sar Images ◽  
Registration Method

Abstract. Automatic registration of optical and synthetic aperture radar (SAR) images is a challenging task due to significant geometric deformation and radiometric differences between two images. To address this issue, this paper proposes an automatic registration method for optical and SAR images based on spatial geometric constraint and structure features. Firstly, the Harris detector with a block strategy is used to extract evenly distributed feature points in the images. Subsequently, a local geometric correction is performed by using the Rational Function Model, which eliminates the rotation and scale differences between optical and SAR images. Secondly, orientated gradient information of images is used to construct a geometric structural feature descriptor. Then, the feature descriptor is transformed into the frequency domain, and the three-dimensional (3-D) phase correlation is used as the similarity metric to achieve correspondences by employing a template matching scheme. Finally, mismatches are eliminated based on spatial geometric constraint relationship between images, followed by a process of geometric correction to achieve the image registration. Experimental results with multiple high-resolution optical and SAR images show that the proposed method can achieve reliable registration accuracy, and outperforms the state of the art methods.

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