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Guided Signal Reconstruction with Application to Image Magnification

Reconstruction Set
Abstract: 

We propose signal reconstruction algorithms which utilize a guiding subspace that represents desired properties of reconstructed signals. Optimal reconstructed signals are shown to belong to a convex bounded set, called the ``reconstruction'' set. Iterative reconstruction algorithms, based on conjugate gradient methods, are developed to approximate optimal reconstructions with low memory and computational costs. Effectiveness of the proposed method is demonstrated with an application to image magnification. [http://arxiv.org/abs/1509.02465]

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Paper Details

Authors:
Akshay Gadde, Hassan Mansour, Dong Tian
Submitted On:
23 February 2016 - 1:44pm
Short Link:
Type:
Presentation Slides
Event:
Presenter's Name:
Andrew Knyazev
Document Year:
2015
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globalsip-15-slides-v2.pdf

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[1] Akshay Gadde, Hassan Mansour, Dong Tian, "Guided Signal Reconstruction with Application to Image Magnification", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/384. Accessed: May. 25, 2018.
@article{384-15,
url = {http://sigport.org/384},
author = {Akshay Gadde; Hassan Mansour; Dong Tian },
publisher = {IEEE SigPort},
title = {Guided Signal Reconstruction with Application to Image Magnification},
year = {2015} }
TY - EJOUR
T1 - Guided Signal Reconstruction with Application to Image Magnification
AU - Akshay Gadde; Hassan Mansour; Dong Tian
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/384
ER -
Akshay Gadde, Hassan Mansour, Dong Tian. (2015). Guided Signal Reconstruction with Application to Image Magnification. IEEE SigPort. http://sigport.org/384
Akshay Gadde, Hassan Mansour, Dong Tian, 2015. Guided Signal Reconstruction with Application to Image Magnification. Available at: http://sigport.org/384.
Akshay Gadde, Hassan Mansour, Dong Tian. (2015). "Guided Signal Reconstruction with Application to Image Magnification." Web.
1. Akshay Gadde, Hassan Mansour, Dong Tian. Guided Signal Reconstruction with Application to Image Magnification [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/384