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Aggelos K. Katsaggelos
Researcher at Northwestern University
Publications - 999
Citations - 28918
Aggelos K. Katsaggelos is an academic researcher from Northwestern University. The author has contributed to research in topics: Image restoration & Image processing. The author has an hindex of 76, co-authored 946 publications receiving 26196 citations. Previous affiliations of Aggelos K. Katsaggelos include University of Stavanger & Delft University of Technology.
Papers
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Journal ArticleDOI
A regularized iterative image restoration algorithm
TL;DR: The adaptively restored images have better quality than the nonadaptively restored ones based on visual observations and on an objective criterion of merit which accounts for the noise masking property of the visual system.
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Noise reduction filters for dynamic image sequences: a review
James C. Brailean,R.P. Kleihorst,S.N. Efstratiadis,Aggelos K. Katsaggelos,Reginald L. Lagendijk +4 more
TL;DR: A thorough review is presented of noise reduction filters for digital image sequences and several algorithms from each of the four categories are implemented and tested on real sequences degraded to various signal-to-noise ratios.
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Iterative Image Restoration Algorithms
TL;DR: This tutorial paper discusses the use of successive-approximation-based iterative restoration algorithms for the removal of linear blurs and noise from images and regularization is introduced as a means for preventing the excessive noise magnification that is typically associated with ill-posed inverse problems such as the deblurring problem.
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Gravity Spy: Integrating Advanced LIGO Detector Characterization, Machine Learning, and Citizen Science
M. Zevin,S. B. Coughlin,Sara Bahaadini,Emre Besler,Neda Rohani,Sarah Allen,Miriam Cabero,Kevin Crowston,Aggelos K. Katsaggelos,Shane L. Larson,Shane L. Larson,Tae Kyoung Lee,Chris Lintott,Tyson Littenberg,Andrew Lundgren,Carsten Østerlund,J. R. Smith,Laura Trouille,Laura Trouille,V. Kalogera +19 more
TL;DR: An innovative project that combines crowdsourcing with machine learning to aid in the challenging task of categorizing all of the glitches recorded by the LIGO detectors is described and a combined method with the aim of improving the efficiency and accuracy of each individual classifier is created.
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Variational Bayesian Super Resolution
TL;DR: This paper addresses the super resolution (SR) problem from a set of degraded low resolution (LR) images to obtain a high resolution (HR) image and proposes novel super resolution methods where the HR image and the motion parameters are estimated simultaneously.