Few views image reconstruction using alternating direction method via ℓ0-norm minimization

2014 ◽  
Vol 24 (3) ◽  
pp. 215-223 ◽  
Author(s):  
Yuli Sun ◽  
Jinxu Tao
Keyword(s):  
Image Reconstruction ◽  
Alternating Direction Method ◽  
Norm Minimization ◽  
Alternating Direction

scholarly journals Distributed Reconstruction via Alternating Direction Method

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Linyuan Wang ◽  
Ailong Cai ◽  
Hanming Zhang ◽  
Bin Yan ◽  
Lei Li ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Image Reconstruction ◽  
Total Variation ◽  
Reconstruction Algorithm ◽  
Alternating Direction Method ◽  
Ct Image ◽  
Total Variation Minimization ◽  
Considerable Research ◽  
Alternating Direction ◽  
Ct Image Reconstruction

With the development of compressive sensing theory, image reconstruction from few-view projections has received considerable research attentions in the field of computed tomography (CT). Total-variation- (TV-) based CT image reconstruction has been shown to be experimentally capable of producing accurate reconstructions from sparse-view data. In this study, a distributed reconstruction algorithm based on TV minimization has been developed. This algorithm is very simple as it uses the alternating direction method. The proposed method can accelerate the alternating direction total variation minimization (ADTVM) algorithm without losing accuracy.

scholarly journals Incomplete Label Distribution Learning

2017 ◽  
Author(s):  
Miao Xu ◽  
Zhi-Hua Zhou
Keyword(s):  
Alternating Direction Method ◽  
Trace Norm ◽  
Method Of Multipliers ◽  
Practical Applications ◽  
Norm Minimization ◽  
Alternating Direction ◽  
Annotation Information ◽  
Real Value ◽  
Label Distribution Learning ◽  
Label Distribution

Label distribution learning (LDL) assumes labels can be associated to an instance to some degree, thus it can learn the relevance of a label to a particular instance. Although LDL has got successful practical applications, one problem with existing LDL methods is that they are designed for data with \emph{complete} supervised information, while in reality, annotation information may be \emph{incomplete}, because assigning each label a real value to indicate its association with a particular instance will result in large cost in labor and time. In this paper, we will solve LDL problem when given \emph{incomplete} supervised information. We propose an objective based on trace norm minimization to exploit the correlation between labels. We develop a proximal gradient descend algorithm and an algorithm based on alternating direction method of multipliers. Experiments validate the effectiveness of our proposal.

Inexact Distributed Reconstruction via Alternating Direction Method

2014 ◽  
Vol 511-512 ◽  
pp. 417-420
Author(s):  
Lin Yuan Wang ◽  
Ai Long Cai ◽  
Bin Yan ◽  
Lei Li ◽  
Han Ming Zhang ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Total Variation ◽  
Reconstruction Algorithm ◽  
Alternating Direction Method ◽  
Experimental Results ◽  
Ct Image ◽  
Total Variation Minimization ◽  
Alternating Direction ◽  
Proximal Points ◽  
Ct Image Reconstruction

Total variation (TV)-based CT image reconstruction has been shown to be experimentally capable of producing accurate reconstructions from sparse-view data. In this study, an inexact distributed reconstruction algorithm based on TV minimization has been developed. The algorithm is relatively simple as it uses the inexact alternating direction method, which involves linearization and proximal points techniques. The outstanding acceleration factor is achieved as the algorithm distributes the data and computation to individual nodes. Experimental results demonstrate that the proposed method can accelerate the alternating direction total variation minimization (ADTVM) algorithm with very little accuracy loss.

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