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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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Proceedings ArticleDOI
Super-resolution of compressed videos using convolutional neural networks
TL;DR: This paper proposes a CNN that is trained on both the spatial and the temporal dimensions of compressed videos to enhance their spatial resolution and pretrained with images, which significantly improves the performance over random initialization.
Journal ArticleDOI
A mathematical model for shape coding with B-splines
TL;DR: An e-cient method for the lossy encoding of object shapes which are given as 8-connect chain codes using a mathematical model that approximate a boundary by a second-order B-spline curve and considers the problem of "nding the curve with the lowest bit-rate for a given distortion".
Journal ArticleDOI
Variational Dirichlet Blur Kernel Estimation
TL;DR: Making use of variational Dirichlet approximation, this paper provides a blur posterior approximation that considers the uncertainty of the estimate and removes noise in the estimated kernel and is very competitive to the state-of-the-art blind image restoration methods.
Journal ArticleDOI
An efficient rate-distortion optimal shape coding approach utilizing a skeleton-based decomposition
TL;DR: A new shape-coding approach is presented, which decouples the shape information into two independent signal data sets; the skeleton and the boundary distance from the skeleton, which allows for a more flexible tradeoff between approximation error and bit budget.
Proceedings ArticleDOI
Simultaneous motion estimation and resolution enhancement of compressed low resolution video
TL;DR: An iterative algorithm for simultaneously estimating the motion field and high resolution frames from a compressed low resolution video sequence by exploiting the existing correlation between high and low resolution frames and information provided by the encoder.