Generalized Alternating Projection for Weighted-$\ell_{2,1}$ Minimization with Applications to Model-Based Compressive Sensing

2014 ◽  
Vol 7 (2) ◽  
pp. 797-823 ◽  
Author(s):  
Xuejun Liao ◽  
Hui Li ◽  
Lawrence Carin
Keyword(s):  
Compressive Sensing ◽  
Model Based ◽  
Alternating Projection

Quantization Noise on Image Reconstruction Using Model-Based Compressive Sensing

2015 ◽  
Vol 13 (4) ◽  
pp. 1167-1177 ◽  
Author(s):  
Julio Cesar Ferreira ◽  
Edna Lucia Flores ◽  
Gilberto Arantes Carrijo
Keyword(s):  
Image Reconstruction ◽  
Compressive Sensing ◽  
Quantization Noise ◽  
Model Based

scholarly journals Projection Matrix Design for Co-Sparse Analysis Model Based Compressive Sensing

2018 ◽  
Vol 159 ◽  
pp. 01061
Author(s):  
Endra Oey ◽  
Dadang Gunawan ◽  
Dodi Sudiana
Keyword(s):  
Compressive Sensing ◽  
Projection Matrix ◽  
Signal Quality ◽  
Tight Frames ◽  
Analysis Model ◽  
Mutual Coherence ◽  
Model Based ◽  
Equiangular Tight Frames ◽  
Simulation Results ◽  
Matrix Design

Co-sparse analysis model based-compressive sensing (CAMBCS) has gained attention in recent years as alternative to conventional sparse synthesis model based (SSMB)-CS. The equivalent operator as counterpart of the equivalent dictionary in the SSMB-CS is introduced in the CAMB-CS as the product of projection matrix and transpose of the analysis dictionary. This paper proposes an algorithm for designing suitable projection matrix for CAMB-CS by minimizing the mutual coherence of the equivalent operator based on equiangular tight frames design. The simulation results show that the CAMB-CS with the proposed projection matrix outperforms the SSMB-CS in terms of the signal quality reconstruction.

A Novel Dictionaries Preconditioning Algorithm for Compressive Sensing

2011 ◽  
Vol 130-134 ◽  
pp. 183-187
Author(s):  
Chao Zhang ◽  
Yi He ◽  
Bo Li
Keyword(s):  
Compressive Sensing ◽  
Projection Method ◽  
Coherence Property ◽  
Construction Algorithm ◽  
Alternating Projection ◽  
Alternating Projection Method

A Novel dictionaries preconditioning algorithm for compressive sensing is proposed in this paper. This algorithm uses alternating projection method to construct sensing and measurement dictionaries with low mutual and cumulative cross coherence. The coherence property of the constructed dictionaries is superior to those constructed by Schnass’ method and by Dictionaries Construction algorithm. The complexity and computation amount is lower than Dictionaries Construction algorithm. The constructed dictionaries improve the performance of OMP and SP algorithms.

Model based variational Bayesian compressive sensing using heavy tailed sparse prior

2016 ◽  
Vol 41 ◽  
pp. 158-167 ◽  
Author(s):  
Zahra Sadeghigol ◽  
Mohammad Hossein Kahaei ◽  
Farzan Haddadi
Keyword(s):  
Compressive Sensing ◽  
Variational Bayesian ◽  
Model Based ◽  
Heavy Tailed ◽  
Sparse Prior

scholarly journals Approximation Algorithms for Model-Based Compressive Sensing

2015 ◽  
Vol 61 (9) ◽  
pp. 5129-5147 ◽  
Author(s):  
Chinmay Hegde ◽  
Piotr Indyk ◽  
Ludwig Schmidt
Keyword(s):  
Approximation Algorithms ◽  
Compressive Sensing ◽  
Model Based
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