Self-adaptive sampling rate assignment and image reconstruction via combination of structured sparsity and non-local total variation priors

2014 ◽  
Vol 29 ◽  
pp. 54-66 ◽  
Author(s):  
Jiawei Chen ◽  
Xiaohua Zhang ◽  
Hongyun Meng
Keyword(s):  
Image Reconstruction ◽  
Total Variation ◽  
Adaptive Sampling ◽  
Sampling Rate ◽  
Structured Sparsity ◽  
Non Local ◽  
Rate Assignment ◽  
Self Adaptive

Non‐local means improves total‐variation constrained photoacoustic image reconstruction

2020 ◽  
Vol 14 (1) ◽  
Author(s):  
Phaneendra K. Yalavarthy ◽  
Sandeep Kumar Kalva ◽  
Manojit Pramanik ◽  
Jaya Prakash
Keyword(s):  
Image Reconstruction ◽  
Total Variation ◽  
Local Means ◽  
Non Local

scholarly journals Improved Compressed Sensing-Based Algorithm for Sparse-View CT Image Reconstruction

2013 ◽  
Vol 2013 ◽  
pp. 1-15 ◽  
Author(s):  
Zangen Zhu ◽  
Khan Wahid ◽  
Paul Babyn ◽  
David Cooper ◽  
Isaac Pratt ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Compressed Sensing ◽  
Total Variation ◽  
Sampling Rate ◽  
Discrete Wavelet ◽  
Ct Image ◽  
Streak Artifact ◽  
Limited Sampling ◽  
Streak Artifacts ◽  
Ct Image Reconstruction

In computed tomography (CT), there are many situations where reconstruction has to be performed with sparse-view data. In sparse-view CT imaging, strong streak artifacts may appear in conventionally reconstructed images due to limited sampling rate that compromises image quality. Compressed sensing (CS) algorithm has shown potential to accurately recover images from highly undersampled data. In the past few years, total-variation-(TV-) based compressed sensing algorithms have been proposed to suppress the streak artifact in CT image reconstruction. In this paper, we propose an efficient compressed sensing-based algorithm for CT image reconstruction from few-view data where we simultaneously minimize three parameters: theℓ1norm, total variation, and a least squares measure. The main feature of our algorithm is the use of two sparsity transforms—discrete wavelet transform and discrete gradient transform. Experiments have been conducted using simulated phantoms and clinical data to evaluate the performance of the proposed algorithm. The results using the proposed scheme show much smaller streaking artifacts and reconstruction errors than other conventional methods.

Data reduction in the ITMS system through a data acquisition model with self-adaptive sampling rate

2008 ◽  
Vol 83 (2-3) ◽  
pp. 358-362 ◽  
Author(s):  
M. Ruiz ◽  
JM. López ◽  
G. de Arcas ◽  
E. Barrera ◽  
R. Melendez ◽  
...  
Keyword(s):  
Data Acquisition ◽  
Data Reduction ◽  
Adaptive Sampling ◽  
Sampling Rate ◽  
Self Adaptive

Self-adaptive sampling rate data acquisition in JET’s correlation reflectometer

2008 ◽  
Vol 79 (10) ◽  
pp. 10F336 ◽  
Author(s):  
G. de Arcas ◽  
J. M. López ◽  
M. Ruiz ◽  
E. Barrera ◽  
J. Vega ◽  
...  
Keyword(s):  
Data Acquisition ◽  
Adaptive Sampling ◽  
Sampling Rate ◽  
Rate Data ◽  
Self Adaptive
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