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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
Using the Kullback-Leibler divergence to combine image priors in Super-Resolution image reconstruction
TL;DR: A unique approximation is obtained here by finding the distribution on the HR image given the observations that minimizes a linear convex combination of the Kullback-Leibler divergences associated with each posterior distribution.
Book ChapterDOI
Super-Resolution from Compressed Video
TL;DR: Improving the spatial resolution of the decoded sequence is no longer the only goal of the recovery algorithm, and the technique is also required to attenuate compression artifacts.
Proceedings ArticleDOI
Simultaneous optimal boundary encoding and variable-length code selection
TL;DR: This paper describes efficient and optimal encoding and representation of object contours by utilizing a differential scheme for the rate and an additive area-based metric for the distortion to formulate the problem as a Lagrangian minimization.
Journal ArticleDOI
Frequency-domain adaptive iterative image restoration and evaluation of the regularization parameter
TL;DR: A nonlinear frequency-domain adaptive regularized iterative image restoration algorithm based on a set-theoretic regularization approach, where bounds on the weighted error residual and stabilizing functional are updated in the frequency domain at each iteration step.
Journal ArticleDOI
An adaptive regularized recursive displacement estimation algorithm
TL;DR: Based on experiments with typical videoconferencing scenes, the improved performance of the proposed algorithm with respect to accuracy, robustness to occlusion and smoothness of the estimated DVF is demonstrated.