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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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Book ChapterDOI
Super Resolution of Multispectral Images Using TV Image Models
TL;DR: A novel algorithm for the pansharpening of multispectral images based on the use of a Total Variation (TV) image prior is proposed and uses the sensor characteristics to model the observation process of both panchromatic and multisectral images.
Proceedings ArticleDOI
Direct: Deep Discriminative Embedding for Clustering of Ligo Data
TL;DR: In this article, a discriminative embedding function is used as a feature extractor for clustering tasks, which can transfer knowledge from the domain of a labeled set of morphologically-distinct images, known as classes, to a new domain within which new classes can potentially be isolated and identified.
Proceedings ArticleDOI
Automatic pigment identification on roman Egyptian paintings by using sparse modeling of hyperspectral images
Neda Rohani,Johanna Salvant,Sara Bahaadini,Oliver Cossairt,Marc Walton,Aggelos K. Katsaggelos +5 more
TL;DR: This paper casts the problem of pigment identification in a novel way by decomposing the spectrum into pure pigments by using hyperspectral reflectance data, and shows qualitatively that the method accurately detects pigment composition for the specific pigments hematite and indigo.
Posted Content
Deep Multi-view Models for Glitch Classification
Sara Bahaadini,Neda Rohani,Scott Coughlin,Michael Zevin,Vicky Kalogera,Aggelos K. Katsaggelos +5 more
TL;DR: A deep multi-view convolutional neural network is proposed to classify glitches automatically to understand their characteristics and origin, which facilitates their removal from the data or from the detector entirely.
Proceedings ArticleDOI
Reconstruction of high-resolution image frames from a sequence of low-resolution and compressed observations
TL;DR: A Bayesian approach is utilized to incorporate the information, and results show a discemable improvement in resolution, as compared to standard interpolation methods.