Bregman regularized statistical image reconstruction method and application to prior image constrained compressed sensing (PICCS)

2013 ◽  
Author(s):  
Yinsheng Li ◽  
Pascal Theriault Lauzier ◽  
Jie Tang ◽  
Guang-Hong Chen
Keyword(s):  
Image Reconstruction ◽  
Compressed Sensing ◽  
Reconstruction Method ◽  
Statistical Image Reconstruction ◽  
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.

Sign in / Sign up

Export Citation Format

Share Document