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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
Event-Driven Video Frame Synthesis
TL;DR: A differentiable fusion model to approximate the dual-modal physical sensing process, unifying a variety of TVFS scenarios, e.g., interpolation, prediction and motion deblur is introduced and a deep learning strategy is developed to enhance the results from the first step, which is referred as a residual "denoising" process.
Journal ArticleDOI
Restoration of severely blurred high range images using stochastic and deterministic relaxation algorithms in compound Gauss–Markov random fields
TL;DR: This paper examines the use of compound Gauss–Markov random fields (CGMRF) to restore severely blurred high range images and proposes two new iterative restoration algorithms which can be considered as extensions of the classical SA and ICM approaches and whose convergence is established.
Journal ArticleDOI
A multicamera setup for generating stereo panoramic video
TL;DR: This paper proposes a new technique for creating stereo panoramic video using a multicamera approach, thus creating a high-resolution output and explores the limitations involved in a practical implementation of the setup, namely the limited number of cameras and the nonzero physical size of real cameras.
Journal ArticleDOI
Compressive holographic video.
Zihao W. Wang,Leonidas Spinoulas,Kuan He,Lei Tian,Oliver Cossairt,Aggelos K. Katsaggelos,Huaijin Chen +6 more
TL;DR: 10× temporal super resolution with multiple depths recovery from a single image is demonstrated for the purpose of recording subtle vibrations and tracking small particles within 5 ms.
Journal ArticleDOI
New termination rule for linear iterative image restoration algorithms
TL;DR: A general technique for determining the spectral filter functions for a given iteration, which is developed and demonstrated by applying it to a linear iterative image restoration algorithm.