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Image Formation

ROBUST AND EFFECTIVE HYPERSPECTRAL PANSHARPENING USING SPATIO-SPECTRAL TOTAL VARIATION


Acquiring high-resolution hyperspectral (HS) images is a very challenging task. To this end, hyperspectral pansharpening techniques have been widely studied, which estimate an HS image of high spatial and spectral resolution (high HS image) from a pair of an HS image of high spectral resolution but low spatial resolution (low HS image) and a high spatial resolution panchromatic (PAN) image.

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Authors:
Shunsuke Ono, Itsuo Kumazawa
Submitted On:
24 April 2018 - 3:30am
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ICASSP2018_poster.pdf

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[1] Shunsuke Ono, Itsuo Kumazawa, "ROBUST AND EFFECTIVE HYPERSPECTRAL PANSHARPENING USING SPATIO-SPECTRAL TOTAL VARIATION", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3159. Accessed: Jul. 22, 2018.
@article{3159-18,
url = {http://sigport.org/3159},
author = {Shunsuke Ono; Itsuo Kumazawa },
publisher = {IEEE SigPort},
title = {ROBUST AND EFFECTIVE HYPERSPECTRAL PANSHARPENING USING SPATIO-SPECTRAL TOTAL VARIATION},
year = {2018} }
TY - EJOUR
T1 - ROBUST AND EFFECTIVE HYPERSPECTRAL PANSHARPENING USING SPATIO-SPECTRAL TOTAL VARIATION
AU - Shunsuke Ono; Itsuo Kumazawa
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3159
ER -
Shunsuke Ono, Itsuo Kumazawa. (2018). ROBUST AND EFFECTIVE HYPERSPECTRAL PANSHARPENING USING SPATIO-SPECTRAL TOTAL VARIATION. IEEE SigPort. http://sigport.org/3159
Shunsuke Ono, Itsuo Kumazawa, 2018. ROBUST AND EFFECTIVE HYPERSPECTRAL PANSHARPENING USING SPATIO-SPECTRAL TOTAL VARIATION. Available at: http://sigport.org/3159.
Shunsuke Ono, Itsuo Kumazawa. (2018). "ROBUST AND EFFECTIVE HYPERSPECTRAL PANSHARPENING USING SPATIO-SPECTRAL TOTAL VARIATION." Web.
1. Shunsuke Ono, Itsuo Kumazawa. ROBUST AND EFFECTIVE HYPERSPECTRAL PANSHARPENING USING SPATIO-SPECTRAL TOTAL VARIATION [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3159

Optimal Stopping Times for Estimating Bernoulli Parameters with Applications to Active Imaging


We address the problem of estimating the parameter of a Bernoulli process. This arises in many applications, including photon-efficient active imaging where each illumination period is regarded as a single Bernoulli trial. We introduce a framework within which to minimize the mean-squared error (MSE) subject to an upper bound on the mean number of trials. This optimization has several simple and intuitive properties when the Bernoulli parameter has a beta prior.

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Authors:
John Murray-Bruce, Vivek K Goyal
Submitted On:
18 July 2018 - 1:20pm
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ICASSP 2018 Poster

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[1] John Murray-Bruce, Vivek K Goyal, "Optimal Stopping Times for Estimating Bernoulli Parameters with Applications to Active Imaging", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3084. Accessed: Jul. 22, 2018.
@article{3084-18,
url = {http://sigport.org/3084},
author = {John Murray-Bruce; Vivek K Goyal },
publisher = {IEEE SigPort},
title = {Optimal Stopping Times for Estimating Bernoulli Parameters with Applications to Active Imaging},
year = {2018} }
TY - EJOUR
T1 - Optimal Stopping Times for Estimating Bernoulli Parameters with Applications to Active Imaging
AU - John Murray-Bruce; Vivek K Goyal
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3084
ER -
John Murray-Bruce, Vivek K Goyal. (2018). Optimal Stopping Times for Estimating Bernoulli Parameters with Applications to Active Imaging. IEEE SigPort. http://sigport.org/3084
John Murray-Bruce, Vivek K Goyal, 2018. Optimal Stopping Times for Estimating Bernoulli Parameters with Applications to Active Imaging. Available at: http://sigport.org/3084.
John Murray-Bruce, Vivek K Goyal. (2018). "Optimal Stopping Times for Estimating Bernoulli Parameters with Applications to Active Imaging." Web.
1. John Murray-Bruce, Vivek K Goyal. Optimal Stopping Times for Estimating Bernoulli Parameters with Applications to Active Imaging [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3084

Optimal Spectrum Estimation and System Trade-Off in Long-Distance Frequency-Modulated Continuous-Wave LIDAR


Frequency-modulated continuous-wave (FMCW) LIDAR is a promising technology for next-generation integrated 3D imaging systems. However, it has been considered difficult to apply FMCW LIDAR for long-distance (>100m) targets, such as those in automotive and airborne applications. Maintaining coherence between the reflected beam from the target and locally forwarded beam becomes a significant challenge for tunable laser design.

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Authors:
Pavan Bhargava, Vladimir Stojanovic
Submitted On:
14 April 2018 - 11:27pm
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icassp2018_fmcw_poster.pdf

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[1] Pavan Bhargava, Vladimir Stojanovic, "Optimal Spectrum Estimation and System Trade-Off in Long-Distance Frequency-Modulated Continuous-Wave LIDAR", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2872. Accessed: Jul. 22, 2018.
@article{2872-18,
url = {http://sigport.org/2872},
author = {Pavan Bhargava; Vladimir Stojanovic },
publisher = {IEEE SigPort},
title = {Optimal Spectrum Estimation and System Trade-Off in Long-Distance Frequency-Modulated Continuous-Wave LIDAR},
year = {2018} }
TY - EJOUR
T1 - Optimal Spectrum Estimation and System Trade-Off in Long-Distance Frequency-Modulated Continuous-Wave LIDAR
AU - Pavan Bhargava; Vladimir Stojanovic
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2872
ER -
Pavan Bhargava, Vladimir Stojanovic. (2018). Optimal Spectrum Estimation and System Trade-Off in Long-Distance Frequency-Modulated Continuous-Wave LIDAR. IEEE SigPort. http://sigport.org/2872
Pavan Bhargava, Vladimir Stojanovic, 2018. Optimal Spectrum Estimation and System Trade-Off in Long-Distance Frequency-Modulated Continuous-Wave LIDAR. Available at: http://sigport.org/2872.
Pavan Bhargava, Vladimir Stojanovic. (2018). "Optimal Spectrum Estimation and System Trade-Off in Long-Distance Frequency-Modulated Continuous-Wave LIDAR." Web.
1. Pavan Bhargava, Vladimir Stojanovic. Optimal Spectrum Estimation and System Trade-Off in Long-Distance Frequency-Modulated Continuous-Wave LIDAR [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2872

CATSEYES: Categorizing Seismic structures with tessellated scattering wavelet networks


As field seismic data sizes are dramatically increasing toward exabytes, automating the labeling of ``structural monads'' --- corresponding to geological patterns and yielding subsurface interpretation --- in a huge amount of available information would drastically reduce interpretation time. Since customary designed features may not account for gradual deformations observable in seismic data, we propose to adapt the wavelet-based scattering network methodology with a tessellation of geophysical images.

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Authors:
Yash BHALGAT, Jean CHARLETY
Submitted On:
13 April 2018 - 9:50am
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Supervised seismic structure classification clustering with wavelet scattering networks

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[1] Yash BHALGAT, Jean CHARLETY, "CATSEYES: Categorizing Seismic structures with tessellated scattering wavelet networks", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2704. Accessed: Jul. 22, 2018.
@article{2704-18,
url = {http://sigport.org/2704},
author = {Yash BHALGAT; Jean CHARLETY },
publisher = {IEEE SigPort},
title = {CATSEYES: Categorizing Seismic structures with tessellated scattering wavelet networks},
year = {2018} }
TY - EJOUR
T1 - CATSEYES: Categorizing Seismic structures with tessellated scattering wavelet networks
AU - Yash BHALGAT; Jean CHARLETY
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2704
ER -
Yash BHALGAT, Jean CHARLETY. (2018). CATSEYES: Categorizing Seismic structures with tessellated scattering wavelet networks. IEEE SigPort. http://sigport.org/2704
Yash BHALGAT, Jean CHARLETY, 2018. CATSEYES: Categorizing Seismic structures with tessellated scattering wavelet networks. Available at: http://sigport.org/2704.
Yash BHALGAT, Jean CHARLETY. (2018). "CATSEYES: Categorizing Seismic structures with tessellated scattering wavelet networks." Web.
1. Yash BHALGAT, Jean CHARLETY. CATSEYES: Categorizing Seismic structures with tessellated scattering wavelet networks [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2704

EVALUATING MULTIEXPOSURE FUSION USING IMAGE INFORMATION

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Authors:
Hisham Rahman, Rajiv Soundararajan, and R. Venkatesh Babu
Submitted On:
12 April 2018 - 2:49pm
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[1] Hisham Rahman, Rajiv Soundararajan, and R. Venkatesh Babu, "EVALUATING MULTIEXPOSURE FUSION USING IMAGE INFORMATION", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2471. Accessed: Jul. 22, 2018.
@article{2471-18,
url = {http://sigport.org/2471},
author = {Hisham Rahman; Rajiv Soundararajan; and R. Venkatesh Babu },
publisher = {IEEE SigPort},
title = {EVALUATING MULTIEXPOSURE FUSION USING IMAGE INFORMATION},
year = {2018} }
TY - EJOUR
T1 - EVALUATING MULTIEXPOSURE FUSION USING IMAGE INFORMATION
AU - Hisham Rahman; Rajiv Soundararajan; and R. Venkatesh Babu
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2471
ER -
Hisham Rahman, Rajiv Soundararajan, and R. Venkatesh Babu. (2018). EVALUATING MULTIEXPOSURE FUSION USING IMAGE INFORMATION. IEEE SigPort. http://sigport.org/2471
Hisham Rahman, Rajiv Soundararajan, and R. Venkatesh Babu, 2018. EVALUATING MULTIEXPOSURE FUSION USING IMAGE INFORMATION. Available at: http://sigport.org/2471.
Hisham Rahman, Rajiv Soundararajan, and R. Venkatesh Babu. (2018). "EVALUATING MULTIEXPOSURE FUSION USING IMAGE INFORMATION." Web.
1. Hisham Rahman, Rajiv Soundararajan, and R. Venkatesh Babu. EVALUATING MULTIEXPOSURE FUSION USING IMAGE INFORMATION [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2471

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
Submitted On:
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: Jul. 22, 2018.
@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

PDF icon ICIP_2017.pdf (127 downloads)

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Authors:
Ziyun Cai, Ling Shao
Submitted On:
20 September 2017 - 1:13pm
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ICIP_2017.pdf

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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: Jul. 22, 2018.
@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: Jul. 22, 2018.
@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: Jul. 22, 2018.
@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
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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: Jul. 22, 2018.
@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

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