Research of Sub-Pixel Image Registration Based on Local-Phase Correlation

2013 ◽  
pp. 507-514 ◽  
Author(s):  
Shiwen Li ◽  
Xiaoxiao Liang
Keyword(s):  
Image Registration ◽  
Phase Correlation ◽  
Local Phase ◽  
Pixel Image

Sub-pixel image registration based on super resolution reconstruction

2017 ◽  
Vol 25 (2) ◽  
pp. 477-484
Author(s):  
李方彪 LI Fang-biao ◽  
何 昕 HE Xin ◽  
魏仲慧 WEI Zhong-hui ◽  
马 鑫 MA Xin
Keyword(s):  
Image Registration ◽  
Super Resolution ◽  
Pixel Image

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

The FPGA implementation of a one-bit-per-pixel image registration algorithm

2014 ◽  
Vol 11 (4) ◽  
pp. 799-815 ◽  
Author(s):  
An Hung Nguyen ◽  
Mark R. Pickering ◽  
Andrew Lambert
Keyword(s):  
Image Registration ◽  
Fpga Implementation ◽  
Registration Algorithm ◽  
Image Registration Algorithm ◽  
Pixel Image

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.

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