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Image Formation

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: Jun. 28, 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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talk slides

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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: Jun. 28, 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
Submitted On:
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: Jun. 28, 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 (39 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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Poster

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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: Jun. 28, 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: Jun. 28, 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: Jun. 28, 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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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: Jun. 28, 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

Fast Voxel Line Update For Time Space Image Reconstruction


For ICASSP 2016 paper.

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/962. Accessed: Jun. 28, 2017.
@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: Jun. 28, 2017.
@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: Jun. 28, 2017.
@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

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