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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
An optimal quadtree-based motion estimation and motion-compensated interpolation scheme for video compression
TL;DR: Results are presented with the proposed QT-based motion estimator which show that for the same DFD energy the proposed estimator uses about 25% fewer bits than the commonly used block matching algorithm.
Journal ArticleDOI
Sparse Bayesian blind image deconvolution with parameter estimation
TL;DR: A novel blind image deconvolution method developed within the Bayesian framework based on a non-convex lpquasi norm with 0
Proceedings ArticleDOI
Pre- and post-processing algorithms for compressed video enhancement
TL;DR: This work surveys the fields of pre- and post-processing techniques for video compression and discusses the current work on compression enhancement algorithms, which are applicable to any compression standard but are discussed within the context of MPEG-2.
Journal ArticleDOI
Hyperparameter estimation in image restoration problems with partially known blurs
Nikolas P. Galatsanos,Vladimir Z. Mesarovic,Rafael Molina,Aggelos K. Katsaggelos,Javier Mateos +4 more
TL;DR: Two iterative algorithms that simultaneously restore the image and estimate the hyperparameters are derived, based on the application of evidence analysis within the hierar- chical Bayesian framework.
Journal ArticleDOI
Bayesian Active Remote Sensing Image Classification
TL;DR: This paper exploits the Bayesian modeling and inference paradigm to tackle the problem of kernel-based remote sensing image classification and proposes an incremental/active learning approach based on three different approaches: the maximum differential of entropies; the minimum distance to decision boundary; and the minimum normalized distance.