scholarly journals A Full-Polarization Radar Image Reconstruction Method with Orthogonal Coding Apertures

2021 ◽  
Vol 13 (22) ◽  
pp. 4626
Author(s):  
Tiehua Zhao ◽  
Qihua Wu ◽  
Feng Zhao ◽  
Zhiming Xu ◽  
Shunping Xiao
Keyword(s):  
Image Reconstruction ◽  
Matching Pursuit ◽  
Coded Aperture ◽  
Reconstruction Method ◽  
Scaled Model ◽  
Polarization Imaging ◽  
Imaging Radar ◽  
Orthogonal Codes ◽  
Image Reconstruction Method ◽  
Aerial Vehicle

Imaging radar is widely applied in both military and civil fields, including remote sensing. In recent years, polarization information has attracted more and more attention in the imaging radar. The orthogonality between different channels is always the core problem for the full-polarization imaging radar. To solve this problem, an image reconstruction method using orthogonal coding apertures technique is proposed for full-polarization imaging radar in this paper. Firstly, the signal model of the orthogonal coding apertures is proposed. This model realizes the ideal time-domain orthogonality between switching two channels by the apertures with two trains of orthogonal codes. Then, a multichannel joint reconstruction method based on compressed sensing is proposed for the imaging processing, which is named the coded aperture simultaneous orthogonal matching pursuit (CAS-OMP) algorithm. The proposed algorithm combines the information of all polarization channels so as to ensure the consistency of the scattering point position obtained by each polarization channel and also improves the reconstruction accuracy. Finally, the simulation experiments using both the simple scaled model of the satellite and measured data of an unmanned aerial vehicle (UAV) are conducted, and the effectiveness of the proposed method is verified.

Image Reconstruction with the Fourier Coefficients for Magnetic Induction Tomography

2020 ◽  
Vol 16 (2) ◽  
pp. 156-163
Author(s):  
Jingwen Wang ◽  
Xu Wang ◽  
Dan Yang ◽  
Kaiyang Wang
Keyword(s):  
Inverse Problem ◽  
Image Reconstruction ◽  
Magnetic Induction ◽  
Fourier Coefficients ◽  
Signal Reconstruction ◽  
Reconstruction Algorithm ◽  
Reconstruction Method ◽  
Measurement Equation ◽  
Image Reconstruction Method ◽  
Magnetic Induction Tomography

Background: Image reconstruction of magnetic induction tomography (MIT) is a typical ill-posed inverse problem, which means that the measurements are always far from enough. Thus, MIT image reconstruction results using conventional algorithms such as linear back projection and Landweber often suffer from limitations such as low resolution and blurred edges. Methods: In this paper, based on the recent finite rate of innovation (FRI) framework, a novel image reconstruction method with MIT system is presented. Results: This is achieved through modeling and sampling the MIT signals in FRI framework, resulting in a few new measurements, namely, fourier coefficients. Because each new measurement contains all the pixel position and conductivity information of the dense phase medium, the illposed inverse problem can be improved, by rebuilding the MIT measurement equation with the measurement voltage and the new measurements. Finally, a sparsity-based signal reconstruction algorithm is presented to reconstruct the original MIT image signal, by solving this new measurement equation. Conclusion: Experiments show that the proposed method has better indicators such as image error and correlation coefficient. Therefore, it is a kind of MIT image reconstruction method with high accuracy.

scholarly journals Effect of PET-MR Inconsistency in the Kernel Image Reconstruction Method

2019 ◽  
Vol 3 (4) ◽  
pp. 400-409 ◽  
Author(s):  
Daniel Deidda ◽  
N. A. Karakatsanis ◽  
Philip M. Robson ◽  
Nikos Efthimiou ◽  
Zahi A. Fayad ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Reconstruction Method ◽  
Image Reconstruction Method
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