Compressed Sensing Image Reconstruction Method Based on Chaotic System

2020 ◽  
pp. 1202-1209
Author(s):  
Yaqin Xie ◽  
Erfu Wang ◽  
Jiayin Yu ◽  
Shiyu Guo ◽  
Xiaomin Zhang
Keyword(s):  
Image Reconstruction ◽  
Compressed Sensing ◽  
Chaotic System ◽  
Reconstruction Method ◽  
Image Reconstruction Method

Passive Millimeter Wave Image Reconstruction Method based on Compressed Sensing Theory

2013 ◽  
Vol 5 (8) ◽  
pp. 1081-1089
Author(s):  
Yilong Zhang ◽  
Yuehua Li ◽  
Sisi Chen ◽  
Jianqiao Wang ◽  
Jianfei Chen ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Compressed Sensing ◽  
Millimeter Wave ◽  
Reconstruction Method ◽  
Image Reconstruction Method ◽  
Wave Image

A fast image reconstruction method based on Bayesian compressed sensing for the undersampled AFM data with noise

2019 ◽  
Vol 30 (2) ◽  
pp. 025402 ◽  
Author(s):  
Yingxu Zhang ◽  
Yingzi Li ◽  
Zhenyu Wang ◽  
Zihang Song ◽  
Rui Lin ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Compressed Sensing ◽  
Reconstruction Method ◽  
Image Reconstruction Method

scholarly journals Research on Image Reconstruction of Compressed Sensing Based on a Multi-Feature Residual Network

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4202
Author(s):  
Ruili Nan ◽  
Guiling Sun ◽  
Zhihong Wang ◽  
Xiangnan Ren
Keyword(s):  
Image Reconstruction ◽  
Compressed Sensing ◽  
Linear Mapping ◽  
Growth Monitoring ◽  
Reconstruction Method ◽  
Residual Network ◽  
Multi Scale ◽  
Reconstruction Methods ◽  
Image Reconstruction Method

In order to solve the problem of how to quickly and accurately obtain crop images during crop growth monitoring, this paper proposes a deep compressed sensing image reconstruction method based on a multi-feature residual network. In this method, the initial reconstructed image obtained by linear mapping is input to a multi-feature residual reconstruction network, and multi-scale convolution is used to autonomously learn different features of the crop image to realize deep reconstruction of the image, and complete the inverse solution of compressed sensing. Compared with traditional image reconstruction methods, the deep learning-based method relaxes the assumptions about the sparsity of the original crop image and converts multiple iterations into deep neural network calculations to obtain higher accuracy. The experimental results show that the compressed sensing image reconstruction method based on the multi-feature residual network proposed in this paper can improve the quality of crop image reconstruction.

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.

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