L
Leonidas Spinoulas
Researcher at University of Southern California
Publications - 39
Citations - 781
Leonidas Spinoulas is an academic researcher from University of Southern California. The author has contributed to research in topics: Compressed sensing & Iterative reconstruction. The author has an hindex of 13, co-authored 39 publications receiving 629 citations. Previous affiliations of Leonidas Spinoulas include Rambus & National Technical University of Athens.
Papers
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Journal ArticleDOI
Deep Fully-Connected Networks for Video Compressive Sensing
TL;DR: In this article, a deep learning framework for video compressive sensing is presented, which enables recovery of video frames in a few seconds at significantly improved reconstruction quality compared to previous approaches.
Journal ArticleDOI
High spatio-temporal resolution video with compressed sensing.
Roman Koller,Lukas Schmid,Nathan Matsuda,Thomas Niederberger,Leonidas Spinoulas,Oliver Cossairt,Guido M. Schuster,Aggelos K. Katsaggelos +7 more
TL;DR: A prototype compressive video camera is presented that encodes scene movement using a translated binary photomask in the optical path, and the use of a printed binary mask allows reconstruction at higher spatial resolutions than has been previously demonstrated.
Journal ArticleDOI
Passive millimeter-wave imaging with compressive sensing
Nachappa Gopalsami,Shaolin Liao,Thomas W. Elmer,Eugene R. Koehl,Alexander Heifetz,Apostolos C. Raptis,Leonidas Spinoulas,Aggelos K. Katsaggelos +7 more
TL;DR: In this article, compressive sensing has been developed for single-pixel optical cameras to significantly reduce the imaging time and at the same time produce high-fidelity images by exploiting the sparsity of the data in some transform domain.
Proceedings ArticleDOI
Compressive passive millimeter-wave imaging
S. D. Babacan,Martin Luessi,Leonidas Spinoulas,Aggelos K. Katsaggelos,Nachappa Gopalsami,Thomas W. Elmer,R. Ahern,Shaolin Liao,A. Raptis +8 more
TL;DR: A novel passive millimeter-wave (PMMW) imaging system designed using compressive sensing principles is presented, and a Bayesian reconstruction algorithm is developed to estimate the original image from these measurements, where the sparsity inherent to typical PMMW images is efficiently exploited.
Journal ArticleDOI
Compressive Blind Image Deconvolution
TL;DR: A novel blind image deconvolution (BID) regularization framework for compressive sensing (CS) based imaging systems capturing blurred images that relies on a constrained optimization technique, and allows the incorporation of existing CS reconstruction algorithms in compressive BID problems.