Pixel-level image reconstruction method of polarization images acquired by multi-aperture imaging systems

2014 ◽  
Author(s):  
Pingli Han ◽  
Fei Liu ◽  
Xiaopeng Shao
Keyword(s):  
Image Reconstruction ◽  
Imaging Systems ◽  
Reconstruction Method ◽  
Image Reconstruction Method

Study on Application of Super-Resolution Image Reconstruction Method Used in Video Image Processing

2012 ◽  
Vol 476-478 ◽  
pp. 1142-1145
Author(s):  
Jing Jia Qi ◽  
Chuan Jun Guo ◽  
Yang Nan
Keyword(s):  
Image Reconstruction ◽  
Performance Improvement ◽  
Super Resolution ◽  
Imaging Systems ◽  
Reconstruction Method ◽  
Low Resolution ◽  
Resolution Image ◽  
Image Reconstruction Method ◽  
Significant Performance ◽  
Resolution Imaging

Super resolution image reconstruction is a computational process of using multiple low-resolution observations to reconstruct a higher resolution image, which differs from improvement of optical devices. With magnification diversity among those low-resolution imagers, significant performance improvement, compared to traditional methods, is demonstrated. Results include fidelity metrics and simulated reconstructions. Performance improvement of super-resolution imaging systems with magnification diversity is studied in this paper.

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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