K
Konstantin Dragomiretskiy
Researcher at University of California, Los Angeles
Publications - 7
Citations - 4308
Konstantin Dragomiretskiy is an academic researcher from University of California, Los Angeles. The author has contributed to research in topics: Compressed sensing & Hilbert–Huang transform. The author has an hindex of 6, co-authored 7 publications receiving 2337 citations.
Papers
More filters
Journal ArticleDOI
Variational Mode Decomposition
TL;DR: This work proposes an entirely non-recursive variational mode decomposition model, where the modes are extracted concurrently and is a generalization of the classic Wiener filter into multiple, adaptive bands.
Book ChapterDOI
Two-Dimensional Variational Mode Decomposition
TL;DR: An entirely non-recursive 2D variational mode decomposition (2D-VMD) model, where the modes are extracted concurrently and the model looks for a number of 2D modes and their respective center frequencies, such that the bandlimited modes reproduce the input image.
Journal ArticleDOI
Two-Dimensional Compact Variational Mode Decomposition
TL;DR: This model decomposes the input signal into modes with narrow Fourier bandwidth; to cope with sharp region boundaries, incompatible with narrow bandwidth, the model introduces binary support functions that act as masks on the narrow-band mode for image recomposition.
Journal ArticleDOI
Mapping Buried Hydrogen-Bonding Networks.
John C. Thomas,Dominic P. Goronzy,Konstantin Dragomiretskiy,Dominique Zosso,Jérôme Gilles,Stanley Osher,Andrea L. Bertozzi,Paul S. Weiss +7 more
TL;DR: It is found that amide-based hydrogen bonds cross molecular domain boundaries and areas of local disorder in buried hydrogen-bonding networks within self-assembled monolayers of 3-mercapto-N-nonylpropionamide.
Journal ArticleDOI
Variational Destriping in Remote Sensing Imagery: Total Variation with L1 Fidelity
TL;DR: A variational method for destriping data acquired by pushbroom-type satellite imaging systems based on the basic principles of regularization and data fidelity with certain constraints using modern methods in variational optimization, namely, total variation, L 1 fidelity, and the alternating direction method of multipliers (ADMM).