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Image Formation

Radioastronomical Image Reconstruction with Regularized Least Squares


Image formation using the data from an array of sensors is a familiar problem in many fields such as radio astronomy, biomedical and geodetic imaging. The problem can be formulated as a least squares (LS) estimation problem and becomes ill-posed at high resolutions, i.e. large number of image pixels. In this paper we propose two regularization methods, one based on weighted truncation of the eigenvalue decomposition of the image deconvolution matrix and the other based on the prior knowledge of the ``dirty image" using the available data.

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Authors:
Shahrzad Naghibzadeh, Ahmad Mouri Sardarabadi, Alle-Jan van der Veen
Submitted On:
23 March 2016 - 6:55pm
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[1] Shahrzad Naghibzadeh, Ahmad Mouri Sardarabadi, Alle-Jan van der Veen, "Radioastronomical Image Reconstruction with Regularized Least Squares", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1003. Accessed: Sep. 18, 2020.
@article{1003-16,
url = {http://sigport.org/1003},
author = {Shahrzad Naghibzadeh; Ahmad Mouri Sardarabadi; Alle-Jan van der Veen },
publisher = {IEEE SigPort},
title = {Radioastronomical Image Reconstruction with Regularized Least Squares},
year = {2016} }
TY - EJOUR
T1 - Radioastronomical Image Reconstruction with Regularized Least Squares
AU - Shahrzad Naghibzadeh; Ahmad Mouri Sardarabadi; Alle-Jan van der Veen
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1003
ER -
Shahrzad Naghibzadeh, Ahmad Mouri Sardarabadi, Alle-Jan van der Veen. (2016). Radioastronomical Image Reconstruction with Regularized Least Squares. IEEE SigPort. http://sigport.org/1003
Shahrzad Naghibzadeh, Ahmad Mouri Sardarabadi, Alle-Jan van der Veen, 2016. Radioastronomical Image Reconstruction with Regularized Least Squares. Available at: http://sigport.org/1003.
Shahrzad Naghibzadeh, Ahmad Mouri Sardarabadi, Alle-Jan van der Veen. (2016). "Radioastronomical Image Reconstruction with Regularized Least Squares." Web.
1. Shahrzad Naghibzadeh, Ahmad Mouri Sardarabadi, Alle-Jan van der Veen. Radioastronomical Image Reconstruction with Regularized Least Squares [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1003

VARIATIONAL BAYESIAN IMAGE FUSION BASED ON COMBINED SPARSE REPRESENTATIONS

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Authors:
Baihong Lin, Xiaoming Tao, Shaoyang Li, Linhao Dong, Jianhua Lu
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23 March 2016 - 11:16am
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Poster.pdf

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[1] Baihong Lin, Xiaoming Tao, Shaoyang Li, Linhao Dong, Jianhua Lu, "VARIATIONAL BAYESIAN IMAGE FUSION BASED ON COMBINED SPARSE REPRESENTATIONS", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/997. Accessed: Sep. 18, 2020.
@article{997-16,
url = {http://sigport.org/997},
author = {Baihong Lin; Xiaoming Tao; Shaoyang Li; Linhao Dong; Jianhua Lu },
publisher = {IEEE SigPort},
title = {VARIATIONAL BAYESIAN IMAGE FUSION BASED ON COMBINED SPARSE REPRESENTATIONS},
year = {2016} }
TY - EJOUR
T1 - VARIATIONAL BAYESIAN IMAGE FUSION BASED ON COMBINED SPARSE REPRESENTATIONS
AU - Baihong Lin; Xiaoming Tao; Shaoyang Li; Linhao Dong; Jianhua Lu
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/997
ER -
Baihong Lin, Xiaoming Tao, Shaoyang Li, Linhao Dong, Jianhua Lu. (2016). VARIATIONAL BAYESIAN IMAGE FUSION BASED ON COMBINED SPARSE REPRESENTATIONS. IEEE SigPort. http://sigport.org/997
Baihong Lin, Xiaoming Tao, Shaoyang Li, Linhao Dong, Jianhua Lu, 2016. VARIATIONAL BAYESIAN IMAGE FUSION BASED ON COMBINED SPARSE REPRESENTATIONS. Available at: http://sigport.org/997.
Baihong Lin, Xiaoming Tao, Shaoyang Li, Linhao Dong, Jianhua Lu. (2016). "VARIATIONAL BAYESIAN IMAGE FUSION BASED ON COMBINED SPARSE REPRESENTATIONS." Web.
1. Baihong Lin, Xiaoming Tao, Shaoyang Li, Linhao Dong, Jianhua Lu. VARIATIONAL BAYESIAN IMAGE FUSION BASED ON COMBINED SPARSE REPRESENTATIONS [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/997

Fast Voxel Line Update For Time Space Image Reconstruction


For ICASSP 2016 paper.

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Authors:
Xiao Wang, K. Aditya Mohan, Sherman Kisner, Charles Bouman, Samuel Midkiff
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22 March 2016 - 4:19am
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[1] Xiao Wang, K. Aditya Mohan, Sherman Kisner, Charles Bouman, Samuel Midkiff, "Fast Voxel Line Update For Time Space Image Reconstruction", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/962. Accessed: Sep. 18, 2020.
@article{962-16,
url = {http://sigport.org/962},
author = {Xiao Wang; K. Aditya Mohan; Sherman Kisner; Charles Bouman; Samuel Midkiff },
publisher = {IEEE SigPort},
title = {Fast Voxel Line Update For Time Space Image Reconstruction},
year = {2016} }
TY - EJOUR
T1 - Fast Voxel Line Update For Time Space Image Reconstruction
AU - Xiao Wang; K. Aditya Mohan; Sherman Kisner; Charles Bouman; Samuel Midkiff
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/962
ER -
Xiao Wang, K. Aditya Mohan, Sherman Kisner, Charles Bouman, Samuel Midkiff. (2016). Fast Voxel Line Update For Time Space Image Reconstruction. IEEE SigPort. http://sigport.org/962
Xiao Wang, K. Aditya Mohan, Sherman Kisner, Charles Bouman, Samuel Midkiff, 2016. Fast Voxel Line Update For Time Space Image Reconstruction. Available at: http://sigport.org/962.
Xiao Wang, K. Aditya Mohan, Sherman Kisner, Charles Bouman, Samuel Midkiff. (2016). "Fast Voxel Line Update For Time Space Image Reconstruction." Web.
1. Xiao Wang, K. Aditya Mohan, Sherman Kisner, Charles Bouman, Samuel Midkiff. Fast Voxel Line Update For Time Space Image Reconstruction [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/962

'FAST VOXEL LINE UPDATE FOR TIME-SPACE IMAGE RECONSTRUCTION


For ICASSP 2016 paper, "Fast Voxel Line Update for Time-Space Image Reconstruction"

Paper Details

Authors:
Xiao Wang, K. Aditya Mohan, Sherman Kisner, Charles Bouman, Samuel Midkiff
Submitted On:
22 March 2016 - 4:19am
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ICASSPTalk.pptx

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[1] Xiao Wang, K. Aditya Mohan, Sherman Kisner, Charles Bouman, Samuel Midkiff, "'FAST VOXEL LINE UPDATE FOR TIME-SPACE IMAGE RECONSTRUCTION", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/960. Accessed: Sep. 18, 2020.
@article{960-16,
url = {http://sigport.org/960},
author = {Xiao Wang; K. Aditya Mohan; Sherman Kisner; Charles Bouman; Samuel Midkiff },
publisher = {IEEE SigPort},
title = {'FAST VOXEL LINE UPDATE FOR TIME-SPACE IMAGE RECONSTRUCTION},
year = {2016} }
TY - EJOUR
T1 - 'FAST VOXEL LINE UPDATE FOR TIME-SPACE IMAGE RECONSTRUCTION
AU - Xiao Wang; K. Aditya Mohan; Sherman Kisner; Charles Bouman; Samuel Midkiff
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/960
ER -
Xiao Wang, K. Aditya Mohan, Sherman Kisner, Charles Bouman, Samuel Midkiff. (2016). 'FAST VOXEL LINE UPDATE FOR TIME-SPACE IMAGE RECONSTRUCTION. IEEE SigPort. http://sigport.org/960
Xiao Wang, K. Aditya Mohan, Sherman Kisner, Charles Bouman, Samuel Midkiff, 2016. 'FAST VOXEL LINE UPDATE FOR TIME-SPACE IMAGE RECONSTRUCTION. Available at: http://sigport.org/960.
Xiao Wang, K. Aditya Mohan, Sherman Kisner, Charles Bouman, Samuel Midkiff. (2016). "'FAST VOXEL LINE UPDATE FOR TIME-SPACE IMAGE RECONSTRUCTION." Web.
1. Xiao Wang, K. Aditya Mohan, Sherman Kisner, Charles Bouman, Samuel Midkiff. 'FAST VOXEL LINE UPDATE FOR TIME-SPACE IMAGE RECONSTRUCTION [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/960

Tomographic Reconstruction of Atmosphric Density with Mumford-Shah Functionals


Knowledge of the three-dimensional spatial structure of Earth's uppermost atmosphere is necessary both to understand its role as a dynamic buffer against the solar-driven environment of interplanetary space as well as to assess the rate of its permanent escape from Earth's gravity through evaporation. The only available means of inferring atmospheric structure at these altitudes is through space-based remote sensing of solar radiation that is resonantly scattered or fluoresced by the ambient atoms.

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Authors:
Lara Waldrop, Farzad Kamalabadi
Submitted On:
20 March 2016 - 11:15am
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ICASSPposter_final1.pdf

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[1] Lara Waldrop, Farzad Kamalabadi, "Tomographic Reconstruction of Atmosphric Density with Mumford-Shah Functionals", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/882. Accessed: Sep. 18, 2020.
@article{882-16,
url = {http://sigport.org/882},
author = {Lara Waldrop; Farzad Kamalabadi },
publisher = {IEEE SigPort},
title = {Tomographic Reconstruction of Atmosphric Density with Mumford-Shah Functionals},
year = {2016} }
TY - EJOUR
T1 - Tomographic Reconstruction of Atmosphric Density with Mumford-Shah Functionals
AU - Lara Waldrop; Farzad Kamalabadi
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/882
ER -
Lara Waldrop, Farzad Kamalabadi. (2016). Tomographic Reconstruction of Atmosphric Density with Mumford-Shah Functionals. IEEE SigPort. http://sigport.org/882
Lara Waldrop, Farzad Kamalabadi, 2016. Tomographic Reconstruction of Atmosphric Density with Mumford-Shah Functionals. Available at: http://sigport.org/882.
Lara Waldrop, Farzad Kamalabadi. (2016). "Tomographic Reconstruction of Atmosphric Density with Mumford-Shah Functionals." Web.
1. Lara Waldrop, Farzad Kamalabadi. Tomographic Reconstruction of Atmosphric Density with Mumford-Shah Functionals [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/882

Spatial Stimuli Gradient Sketch Model


The inability of automated edge detection methods inspired from primal sketch models to accurately calculate object edges under the influence of pixel noise is an open problem. Extending the principles of image perception i.e. Weber-Fechner law, and Sheperd similarity law, we propose a new edge detection method and formulation that use perceived brightness and neighbourhood similarity calculations in the determination of robust object edges.

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Authors:
Joshin John Mathew
Submitted On:
23 February 2016 - 1:44pm
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Spatial Stimuli Gradient Sketch Model (SSGSM)_0 (1).pdf

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[1] Joshin John Mathew, "Spatial Stimuli Gradient Sketch Model", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/366. Accessed: Sep. 18, 2020.
@article{366-15,
url = {http://sigport.org/366},
author = {Joshin John Mathew },
publisher = {IEEE SigPort},
title = {Spatial Stimuli Gradient Sketch Model},
year = {2015} }
TY - EJOUR
T1 - Spatial Stimuli Gradient Sketch Model
AU - Joshin John Mathew
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/366
ER -
Joshin John Mathew. (2015). Spatial Stimuli Gradient Sketch Model. IEEE SigPort. http://sigport.org/366
Joshin John Mathew, 2015. Spatial Stimuli Gradient Sketch Model. Available at: http://sigport.org/366.
Joshin John Mathew. (2015). "Spatial Stimuli Gradient Sketch Model." Web.
1. Joshin John Mathew. Spatial Stimuli Gradient Sketch Model [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/366

Augmented Lagrangian without alternating directions: practical algorithms for inverse problems in imaging


Several problems in signal processing and machine learning can be casted as optimization problems. In many cases, they are of large-scale, nonlinear, have constraints, and nonsmooth in the unknown parameters. There exists plethora of fast algorithms for smooth convex optimization, but these algorithms are not readily applicable to nonsmooth problems, which has led to a considerable amount of research in this direction. In this paper, we propose a general algorithm for nonsmooth bound-constrained convex optimization problems.

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Authors:
Loic Denis, Eric Thiebaut, J-M Becker
Submitted On:
23 February 2016 - 1:38pm
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draft_ICIP_poster2.pdf

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[1] Loic Denis, Eric Thiebaut, J-M Becker, "Augmented Lagrangian without alternating directions: practical algorithms for inverse problems in imaging", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/222. Accessed: Sep. 18, 2020.
@article{222-15,
url = {http://sigport.org/222},
author = {Loic Denis; Eric Thiebaut; J-M Becker },
publisher = {IEEE SigPort},
title = {Augmented Lagrangian without alternating directions: practical algorithms for inverse problems in imaging},
year = {2015} }
TY - EJOUR
T1 - Augmented Lagrangian without alternating directions: practical algorithms for inverse problems in imaging
AU - Loic Denis; Eric Thiebaut; J-M Becker
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/222
ER -
Loic Denis, Eric Thiebaut, J-M Becker. (2015). Augmented Lagrangian without alternating directions: practical algorithms for inverse problems in imaging. IEEE SigPort. http://sigport.org/222
Loic Denis, Eric Thiebaut, J-M Becker, 2015. Augmented Lagrangian without alternating directions: practical algorithms for inverse problems in imaging. Available at: http://sigport.org/222.
Loic Denis, Eric Thiebaut, J-M Becker. (2015). "Augmented Lagrangian without alternating directions: practical algorithms for inverse problems in imaging." Web.
1. Loic Denis, Eric Thiebaut, J-M Becker. Augmented Lagrangian without alternating directions: practical algorithms for inverse problems in imaging [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/222

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