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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
Computational multifocal microscopy.
Kuan He,Zihao W. Wang,Xiang Huang,Xiaolei Wang,Seunghwan Yoo,Pablo Ruiz,Itay Gdor,Alan Selewa,Nicola J. Ferrier,Norbert F. Scherer,Mark Hereld,Aggelos K. Katsaggelos,Oliver Cossairt +12 more
TL;DR: In this article, a unified computational framework was proposed to simplify the imaging system and achieve 3D reconstruction via computation by removing optical elements for correcting chromatic aberrations and redesigning the multifocal grating to enlarge the tracking area.
Journal ArticleDOI
Shape coding using temporal correlation and joint VLC optimization
TL;DR: This paper investigates ways to explore the between frame correlation of shape information within the framework of an operationally rate-distortion (ORD) optimized coder by utilizing a novel criterion for selecting global object motion vectors, which improves the efficiency.
Journal ArticleDOI
Development of a highly mobile and versatile large MA‐XRF scanner for in situ analyses of painted work of arts
Emeline Pouyet,Nicholas C. Barbi,Henry H. Chopp,Owen Healy,Aggelos K. Katsaggelos,Sophia Moak,Rick Mott,Marc Vermeulen,Marc Walton +8 more
Journal ArticleDOI
Adaptive Image Sampling using Deep Learning and its Application on X-Ray Fluorescence Image Reconstruction
TL;DR: An adaptive image sampling algorithm based on Deep Learning which is jointly trained with an image inpainting network is presented, able to effectively sample the image and achieve a better reconstruction accuracy than that of existing methods.
Proceedings ArticleDOI
Preprocessing of compressed digital video
TL;DR: In this paper, a pre-processing technique that is loosely coupled to the quantization decisions of a rate control mechanism is proposed to improve the performance of video compression system by removing spurious noise and insignificant features from the original images.