Block Coordinate Descent Based Algorithm for Image Reconstruction in Photoacoustic Tomography

2019 ◽  
pp. 81-93
Author(s):  
Anjali Gupta ◽  
Ashok Kumar Kajla ◽  
Ramesh Chandra Bansal
Keyword(s):  
Image Reconstruction ◽  
Coordinate Descent ◽  
Photoacoustic Tomography ◽  
Block Coordinate Descent

scholarly journals Efficient Asynchronous Semi-stochastic Block Coordinate Descent Methods for Large-Scale SVD

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Fanhua Shang ◽  
Zhihui Zhang ◽  
Yuanyuan Liu ◽  
Hongying Liua ◽  
Jing Xu
Keyword(s):  
Large Scale ◽  
Coordinate Descent ◽  
Descent Methods ◽  
Block Coordinate Descent

scholarly journals A Block Coordinate Descent-Based Projected Gradient Algorithm for Orthogonal Non-Negative Matrix Factorization

Mathematics ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 540
Author(s):  
Soodabeh Asadi ◽  
Janez Povh
Keyword(s):  
Matrix Factorization ◽  
Synthetic Data ◽  
Coordinate Descent ◽  
The Other ◽  
Gradient Algorithm ◽  
Block Coordinate Descent ◽  
Projected Gradient Method ◽  
Projected Gradient ◽  
Factorization Problem ◽  
Non Negative Matrix Factorization

This article uses the projected gradient method (PG) for a non-negative matrix factorization problem (NMF), where one or both matrix factors must have orthonormal columns or rows. We penalize the orthonormality constraints and apply the PG method via a block coordinate descent approach. This means that at a certain time one matrix factor is fixed and the other is updated by moving along the steepest descent direction computed from the penalized objective function and projecting onto the space of non-negative matrices. Our method is tested on two sets of synthetic data for various values of penalty parameters. The performance is compared to the well-known multiplicative update (MU) method from Ding (2006), and with a modified global convergent variant of the MU algorithm recently proposed by Mirzal (2014). We provide extensive numerical results coupled with appropriate visualizations, which demonstrate that our method is very competitive and usually outperforms the other two methods.

A Unified-Model via Block Coordinate Descent for Learning the Importance of Filter

2021 ◽  
Author(s):  
Qinghua Li ◽  
Xue Zhang ◽  
Cuiping Li ◽  
Hong Chen
Keyword(s):  
Unified Model ◽  
Coordinate Descent ◽  
Block Coordinate Descent

Half-Time Image Reconstruction in Photoacoustic Tomography

2017 ◽  
pp. 155-164
Author(s):  
Mark A. Anastasio ◽  
Jin Zhang ◽  
Xiaochuan Pan ◽  
Lihong V. Wang
Keyword(s):  
Image Reconstruction ◽  
Photoacoustic Tomography ◽  
Half Time ◽  
Time Image

Bayesian Image Reconstruction in Quantitative Photoacoustic Tomography

2013 ◽  
Vol 32 (12) ◽  
pp. 2287-2298 ◽  
Author(s):  
Tanja Tarvainen ◽  
Aki Pulkkinen ◽  
Ben T. Cox ◽  
Jari P. Kaipio ◽  
Simon R. Arridge
Keyword(s):  
Image Reconstruction ◽  
Photoacoustic Tomography ◽  
Bayesian Image Reconstruction
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