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Image Formation

ADMM Penalty Parameter Selection with Krylov Subspace Recycling Technique for Sparse Coding


The alternating direction method of multipliers (ADMM) has been widely used for a very wide variety of imaging inverse problems. One of the disadvantages of this method, however, is the need to select an algorithm parameter, the penalty parameter, that has a significant effect on the rate of convergence of the algorithm. Although a number of heuristic methods have been proposed, as yet there is no general theory providing a good choice of this parameter for all problems.

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Authors:
Youzuo Lin, Brendt Wohlberg, Velimir Vesselinov
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3 October 2017 - 6:45pm
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[1] Youzuo Lin, Brendt Wohlberg, Velimir Vesselinov, "ADMM Penalty Parameter Selection with Krylov Subspace Recycling Technique for Sparse Coding", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2254. Accessed: Oct. 20, 2017.
@article{2254-17,
url = {http://sigport.org/2254},
author = {Youzuo Lin; Brendt Wohlberg; Velimir Vesselinov },
publisher = {IEEE SigPort},
title = {ADMM Penalty Parameter Selection with Krylov Subspace Recycling Technique for Sparse Coding},
year = {2017} }
TY - EJOUR
T1 - ADMM Penalty Parameter Selection with Krylov Subspace Recycling Technique for Sparse Coding
AU - Youzuo Lin; Brendt Wohlberg; Velimir Vesselinov
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2254
ER -
Youzuo Lin, Brendt Wohlberg, Velimir Vesselinov. (2017). ADMM Penalty Parameter Selection with Krylov Subspace Recycling Technique for Sparse Coding. IEEE SigPort. http://sigport.org/2254
Youzuo Lin, Brendt Wohlberg, Velimir Vesselinov, 2017. ADMM Penalty Parameter Selection with Krylov Subspace Recycling Technique for Sparse Coding. Available at: http://sigport.org/2254.
Youzuo Lin, Brendt Wohlberg, Velimir Vesselinov. (2017). "ADMM Penalty Parameter Selection with Krylov Subspace Recycling Technique for Sparse Coding." Web.
1. Youzuo Lin, Brendt Wohlberg, Velimir Vesselinov. ADMM Penalty Parameter Selection with Krylov Subspace Recycling Technique for Sparse Coding [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2254

RGB-D DATA FUSION IN COMPLEX SPACE


Most of the RGB-D fusion methods extract features from RGB
data and depth data separately and then simply concatenate
them or encode these two kinds of features. Such frameworks
cannot explore the correlation between the RGB pixels and
their corresponding depth pixels. Motivated by the physical
concept that range data correspond to the phase change and
color information corresponds to the intensity, we first project
raw RGB-D data into a complex space and then jointly extract
features from the fused RGB-D images. Consequently, the

ICIP_2017.pdf

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Authors:
Ziyun Cai, Ling Shao
Submitted On:
20 September 2017 - 1:13pm
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[1] Ziyun Cai, Ling Shao, "RGB-D DATA FUSION IN COMPLEX SPACE", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2240. Accessed: Oct. 20, 2017.
@article{2240-17,
url = {http://sigport.org/2240},
author = {Ziyun Cai; Ling Shao },
publisher = {IEEE SigPort},
title = {RGB-D DATA FUSION IN COMPLEX SPACE},
year = {2017} }
TY - EJOUR
T1 - RGB-D DATA FUSION IN COMPLEX SPACE
AU - Ziyun Cai; Ling Shao
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2240
ER -
Ziyun Cai, Ling Shao. (2017). RGB-D DATA FUSION IN COMPLEX SPACE. IEEE SigPort. http://sigport.org/2240
Ziyun Cai, Ling Shao, 2017. RGB-D DATA FUSION IN COMPLEX SPACE. Available at: http://sigport.org/2240.
Ziyun Cai, Ling Shao. (2017). "RGB-D DATA FUSION IN COMPLEX SPACE." Web.
1. Ziyun Cai, Ling Shao. RGB-D DATA FUSION IN COMPLEX SPACE [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2240

Robust Plane-based Calibration for linear cameras


A linear, or 1D, camera is a type of camera that sweeps a linear sensor array over the scene, rather than capturing the scene using a single impression on a 2D sensor array.
They are often used in satellite imagery, industrial inspection, or hyperspectral imaging.
In satellite imaging calibration is often done through a collection of ground points for which the 3D locations are known.
In other applications, e.g. hyperspectral imaging, such known points are not available and annotating many different points is onerous.

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Authors:
Simon Donné, Hiep Luong, Stijn Dhondt, Nathalie Wuyts, Dirk Inzé, Bart Goossens, Wilfried Philips
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16 September 2017 - 2:15am
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[1] Simon Donné, Hiep Luong, Stijn Dhondt, Nathalie Wuyts, Dirk Inzé, Bart Goossens, Wilfried Philips, "Robust Plane-based Calibration for linear cameras", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2179. Accessed: Oct. 20, 2017.
@article{2179-17,
url = {http://sigport.org/2179},
author = {Simon Donné; Hiep Luong; Stijn Dhondt; Nathalie Wuyts; Dirk Inzé; Bart Goossens; Wilfried Philips },
publisher = {IEEE SigPort},
title = {Robust Plane-based Calibration for linear cameras},
year = {2017} }
TY - EJOUR
T1 - Robust Plane-based Calibration for linear cameras
AU - Simon Donné; Hiep Luong; Stijn Dhondt; Nathalie Wuyts; Dirk Inzé; Bart Goossens; Wilfried Philips
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2179
ER -
Simon Donné, Hiep Luong, Stijn Dhondt, Nathalie Wuyts, Dirk Inzé, Bart Goossens, Wilfried Philips. (2017). Robust Plane-based Calibration for linear cameras. IEEE SigPort. http://sigport.org/2179
Simon Donné, Hiep Luong, Stijn Dhondt, Nathalie Wuyts, Dirk Inzé, Bart Goossens, Wilfried Philips, 2017. Robust Plane-based Calibration for linear cameras. Available at: http://sigport.org/2179.
Simon Donné, Hiep Luong, Stijn Dhondt, Nathalie Wuyts, Dirk Inzé, Bart Goossens, Wilfried Philips. (2017). "Robust Plane-based Calibration for linear cameras." Web.
1. Simon Donné, Hiep Luong, Stijn Dhondt, Nathalie Wuyts, Dirk Inzé, Bart Goossens, Wilfried Philips. Robust Plane-based Calibration for linear cameras [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2179

Resolution Enhancement for Hyperspectral Images: A Super-Resolution and Fusion Approach


Many remote sensing applications require a high-resolution hyperspectral image. However, resolutions of most hyperspectral imagers are limited to tens of meters. Existing resolution enhancement techniques either acquire additional multispectral band images or use a pan band image. The former poses hardware challenges, whereas the latter has limited performance. In this paper, we present a new resolution enhancement method that only requires a color image.

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Authors:
Chiman Kwan, Joon Hee Choi, Stanley Chan, Jin Zhou, and Bence Budavari
Submitted On:
8 March 2017 - 12:29pm
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ICASSP pansharpening - presentation.pdf

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[1] Chiman Kwan, Joon Hee Choi, Stanley Chan, Jin Zhou, and Bence Budavari, "Resolution Enhancement for Hyperspectral Images: A Super-Resolution and Fusion Approach", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1708. Accessed: Oct. 20, 2017.
@article{1708-17,
url = {http://sigport.org/1708},
author = {Chiman Kwan; Joon Hee Choi; Stanley Chan; Jin Zhou; and Bence Budavari },
publisher = {IEEE SigPort},
title = {Resolution Enhancement for Hyperspectral Images: A Super-Resolution and Fusion Approach},
year = {2017} }
TY - EJOUR
T1 - Resolution Enhancement for Hyperspectral Images: A Super-Resolution and Fusion Approach
AU - Chiman Kwan; Joon Hee Choi; Stanley Chan; Jin Zhou; and Bence Budavari
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1708
ER -
Chiman Kwan, Joon Hee Choi, Stanley Chan, Jin Zhou, and Bence Budavari. (2017). Resolution Enhancement for Hyperspectral Images: A Super-Resolution and Fusion Approach. IEEE SigPort. http://sigport.org/1708
Chiman Kwan, Joon Hee Choi, Stanley Chan, Jin Zhou, and Bence Budavari, 2017. Resolution Enhancement for Hyperspectral Images: A Super-Resolution and Fusion Approach. Available at: http://sigport.org/1708.
Chiman Kwan, Joon Hee Choi, Stanley Chan, Jin Zhou, and Bence Budavari. (2017). "Resolution Enhancement for Hyperspectral Images: A Super-Resolution and Fusion Approach." Web.
1. Chiman Kwan, Joon Hee Choi, Stanley Chan, Jin Zhou, and Bence Budavari. Resolution Enhancement for Hyperspectral Images: A Super-Resolution and Fusion Approach [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1708

Accelerated dual gradient-based methods for total variation image denoising/deblurring problems

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Authors:
Donghwan Kim, Jeffrey A Fessler
Submitted On:
7 March 2017 - 12:32pm
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[1] Donghwan Kim, Jeffrey A Fessler, "Accelerated dual gradient-based methods for total variation image denoising/deblurring problems", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1688. Accessed: Oct. 20, 2017.
@article{1688-17,
url = {http://sigport.org/1688},
author = {Donghwan Kim; Jeffrey A Fessler },
publisher = {IEEE SigPort},
title = {Accelerated dual gradient-based methods for total variation image denoising/deblurring problems},
year = {2017} }
TY - EJOUR
T1 - Accelerated dual gradient-based methods for total variation image denoising/deblurring problems
AU - Donghwan Kim; Jeffrey A Fessler
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1688
ER -
Donghwan Kim, Jeffrey A Fessler. (2017). Accelerated dual gradient-based methods for total variation image denoising/deblurring problems. IEEE SigPort. http://sigport.org/1688
Donghwan Kim, Jeffrey A Fessler, 2017. Accelerated dual gradient-based methods for total variation image denoising/deblurring problems. Available at: http://sigport.org/1688.
Donghwan Kim, Jeffrey A Fessler. (2017). "Accelerated dual gradient-based methods for total variation image denoising/deblurring problems." Web.
1. Donghwan Kim, Jeffrey A Fessler. Accelerated dual gradient-based methods for total variation image denoising/deblurring problems [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1688

ORGB: OFFSET CORRECTION IN RGB COLOR SPACE FOR ILLUMINATION-ROBUST IMAGE PROCESSING


Single materials have colors which form straight lines in RGB space. However, in severe shadow cases, those lines do not intersect the origin, which is inconsistent with the description of most literature. This paper is concerned with the detection and correction of the offset between the intersection and origin. First, we analyze the reason for forming that offset via an optical imaging model. Second, we present a simple and effective way to detect and remove the offset.

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Authors:
Zhenqiang Ying, Ge Li, Sixin Wen, Guozhen Tan
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6 March 2017 - 8:12am
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zqying_icassp2017_poster.pdf

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[1] Zhenqiang Ying, Ge Li, Sixin Wen, Guozhen Tan, "ORGB: OFFSET CORRECTION IN RGB COLOR SPACE FOR ILLUMINATION-ROBUST IMAGE PROCESSING", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1647. Accessed: Oct. 20, 2017.
@article{1647-17,
url = {http://sigport.org/1647},
author = {Zhenqiang Ying; Ge Li; Sixin Wen; Guozhen Tan },
publisher = {IEEE SigPort},
title = {ORGB: OFFSET CORRECTION IN RGB COLOR SPACE FOR ILLUMINATION-ROBUST IMAGE PROCESSING},
year = {2017} }
TY - EJOUR
T1 - ORGB: OFFSET CORRECTION IN RGB COLOR SPACE FOR ILLUMINATION-ROBUST IMAGE PROCESSING
AU - Zhenqiang Ying; Ge Li; Sixin Wen; Guozhen Tan
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1647
ER -
Zhenqiang Ying, Ge Li, Sixin Wen, Guozhen Tan. (2017). ORGB: OFFSET CORRECTION IN RGB COLOR SPACE FOR ILLUMINATION-ROBUST IMAGE PROCESSING. IEEE SigPort. http://sigport.org/1647
Zhenqiang Ying, Ge Li, Sixin Wen, Guozhen Tan, 2017. ORGB: OFFSET CORRECTION IN RGB COLOR SPACE FOR ILLUMINATION-ROBUST IMAGE PROCESSING. Available at: http://sigport.org/1647.
Zhenqiang Ying, Ge Li, Sixin Wen, Guozhen Tan. (2017). "ORGB: OFFSET CORRECTION IN RGB COLOR SPACE FOR ILLUMINATION-ROBUST IMAGE PROCESSING." Web.
1. Zhenqiang Ying, Ge Li, Sixin Wen, Guozhen Tan. ORGB: OFFSET CORRECTION IN RGB COLOR SPACE FOR ILLUMINATION-ROBUST IMAGE PROCESSING [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1647

Phase Congruency for Image Understanding with Applications in Computational Seismic Interpretation


Phase Congruency (PC) can highlight small discontinuities in images with varying illumination and contrast using the congruency of phase in Fourier components. PC can not only detect the subtle variations in the image intensity but can also highlight the anomalous values to develop a deeper understanding of the images content and context. In this paper, we propose a new method based on PC for computational seismic interpretation with an application to subsurface structures delineation within migrated seismic volumes.

ICASSP_20170214.pdf

PDF icon Poster (85 downloads)

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Authors:
Muhammad Amir Shafiq, Yazeed Alaudah, Ghassan AlRegib, and Mohammad Deriche
Submitted On:
1 March 2017 - 6:01pm
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[1] Muhammad Amir Shafiq, Yazeed Alaudah, Ghassan AlRegib, and Mohammad Deriche, "Phase Congruency for Image Understanding with Applications in Computational Seismic Interpretation", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1559. Accessed: Oct. 20, 2017.
@article{1559-17,
url = {http://sigport.org/1559},
author = {Muhammad Amir Shafiq; Yazeed Alaudah; Ghassan AlRegib; and Mohammad Deriche },
publisher = {IEEE SigPort},
title = {Phase Congruency for Image Understanding with Applications in Computational Seismic Interpretation},
year = {2017} }
TY - EJOUR
T1 - Phase Congruency for Image Understanding with Applications in Computational Seismic Interpretation
AU - Muhammad Amir Shafiq; Yazeed Alaudah; Ghassan AlRegib; and Mohammad Deriche
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1559
ER -
Muhammad Amir Shafiq, Yazeed Alaudah, Ghassan AlRegib, and Mohammad Deriche. (2017). Phase Congruency for Image Understanding with Applications in Computational Seismic Interpretation. IEEE SigPort. http://sigport.org/1559
Muhammad Amir Shafiq, Yazeed Alaudah, Ghassan AlRegib, and Mohammad Deriche, 2017. Phase Congruency for Image Understanding with Applications in Computational Seismic Interpretation. Available at: http://sigport.org/1559.
Muhammad Amir Shafiq, Yazeed Alaudah, Ghassan AlRegib, and Mohammad Deriche. (2017). "Phase Congruency for Image Understanding with Applications in Computational Seismic Interpretation." Web.
1. Muhammad Amir Shafiq, Yazeed Alaudah, Ghassan AlRegib, and Mohammad Deriche. Phase Congruency for Image Understanding with Applications in Computational Seismic Interpretation [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1559

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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ICASSP_Poster.pdf

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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/1004. Accessed: Oct. 20, 2017.
@article{1004-16,
url = {http://sigport.org/1004},
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/1004
ER -
Shahrzad Naghibzadeh, Ahmad Mouri Sardarabadi, Alle-Jan van der Veen. (2016). Radioastronomical Image Reconstruction with Regularized Least Squares. IEEE SigPort. http://sigport.org/1004
Shahrzad Naghibzadeh, Ahmad Mouri Sardarabadi, Alle-Jan van der Veen, 2016. Radioastronomical Image Reconstruction with Regularized Least Squares. Available at: http://sigport.org/1004.
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/1004

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.

Paper Details

Authors:
Shahrzad Naghibzadeh, Ahmad Mouri Sardarabadi, Alle-Jan van der Veen
Submitted On:
23 March 2016 - 6:55pm
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ICASSP_Poster.pdf

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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: Oct. 20, 2017.
@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
Submitted On:
23 March 2016 - 11:16am
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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: Oct. 20, 2017.
@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

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