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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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High-resolution images from low-resolution compressed video
TL;DR: This survey surveys the field of super resolution (SR) processing for compressed video and develops and presents all techniques within the Bayesian framework, and surveys models for the acquisition and compression systems.
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Stochastic methods for joint registration, restoration, and interpolation of multiple undersampled images
TL;DR: This work proposes two algorithms for the problem of obtaining a single high-resolution image from multiple noisy, blurred, and undersampled images based on a Bayesian formulation that is implemented via the expectation maximization algorithm and a maximum a posteriori formulation.
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Regularized constrained total least squares image restoration
TL;DR: Object and visual comparisons are presented with the linear minimum mean-squared-error (LMMSE) and the regularized least-squares (RLS) estimator and show that the RCTLS estimator reduces significantly ringing artifacts around edges as compared to the two other approaches.
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Automated Atrial Fibrillation Detection using a Hybrid CNN-LSTM Network on Imbalanced ECG Datasets
Georgios Petmezas,K. Haris,Leandros Stefanopoulos,Vassilis Kilintzis,Andreas Tzavelis,John A. Rogers,Aggelos K. Katsaggelos,Nicos Maglaveras,Nicos Maglaveras +8 more
TL;DR: A novel hybrid neural model utilizing focal loss, an improved version of cross-entropy loss, to deal with training data imbalance is proposed, which can aid clinicians to detect common atrial fibrillation in real-time on routine screening ECG.
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Joint Source Adaptation and Resource Allocation for Multi-User Wireless Video Streaming
TL;DR: A joint adaptation, resource allocation and scheduling (JARS) algorithm, which allocates the communication resource based on the video users' quality of service, adapts video sources based on smart summarization, and schedules the transmissions to meet the frame delivery deadlines.