Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Konstantin Dragomiretskiy"'
Publikováno v:
Remote Sensing, Vol 10, Iss 2, p 300 (2018)
This paper introduces a variational method for destriping data acquired by pushbroom-type satellite imaging systems. The model leverages sparsity in signals and is based on current research in sparse optimization and compressed sensing. It is based o
Externí odkaz:
https://doaj.org/article/9545ef44fadc47efa6eaed8d39657983
Publikováno v:
Journal of Mathematical Imaging and Vision. 58:294-320
Decomposing multidimensional signals, such as images, into spatially compact, potentially overlapping modes of essentially wavelike nature makes these components accessible for further downstream analysis. This decomposition enables space---frequency
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2d29dd20cbc732482fe2c5167d38d952
https://doi.org/10.1007/s10851-017-0710-z
https://doi.org/10.1007/s10851-017-0710-z
Publikováno v:
Remote Sensing, Vol 10, Iss 2, p 300 (2018)
Remote Sensing; Volume 10; Issue 2; Pages: 300
Remote Sensing; Volume 10; Issue 2; Pages: 300
This paper introduces a variational method for destriping data acquired by pushbroom-type satellite imaging systems. The model leverages sparsity in signals and is based on current research in sparse optimization and compressed sensing. It is based o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4e1deabd4836414808891ee68e5759ff
http://www.mdpi.com/2072-4292/10/2/300
http://www.mdpi.com/2072-4292/10/2/300
Publikováno v:
IGARSS
This paper introduces a variational method for destriping data acquired by pushbroom-type satellite imaging systems. The model leverages sparsity in signals and is based on current research in sparse optimization and compressed sensing. It is based o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a7ab17b0f69b77687437b00e696abdfe
https://doi.org/10.1109/igarss.2017.8127000
https://doi.org/10.1109/igarss.2017.8127000
Publikováno v:
IEEE TRANSACTIONS ON SIGNAL PROCESSING
During the late 1990s, Huang introduced the algorithm called Empirical Mode Decomposition, which is widely used today to recursively decompose a signal into different modes of unknown but separate spectral bands. EMD is known for limitations like sen
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ca9ab961d93733498064922bd5d4106c
Autor:
Paul S. Weiss, Dominique Zosso, Stanley Osher, John C. Thomas, Dominic P. Goronzy, Konstantin Dragomiretskiy, Andrea L. Bertozzi, Jérôme Gilles
Publikováno v:
ACS nano, vol 10, iss 5
Thomas, JC; Goronzy, DP; Dragomiretskiy, K; Zosso, D; Gilles, J; Osher, SJ; et al.(2016). Mapping Buried Hydrogen-Bonding Networks. ACS NANO, 10(5), 5446-5451. doi: 10.1021/acsnano.6b01717. UCLA: Retrieved from: http://www.escholarship.org/uc/item/6pw3c3t8
Thomas, JC; Goronzy, DP; Dragomiretskiy, K; Zosso, D; Gilles, J; Osher, SJ; et al.(2016). Mapping Buried Hydrogen-Bonding Networks. ACS NANO, 10(5), 5446-5451. doi: 10.1021/acsnano.6b01717. UCLA: Retrieved from: http://www.escholarship.org/uc/item/6pw3c3t8
We map buried hydrogen-bonding networks within self-assembled monolayers of 3-mercapto-N-nonylpropionamide on Au{111}. The contributing interactions include the buried S-Au bonds at the substrate surface and the buried plane of linear networks of hyd
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e0dc0f7d2e13cd8c9c775ff1aac3da77
https://escholarship.org/uc/item/6pw3c3t8
https://escholarship.org/uc/item/6pw3c3t8
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783319146119
EMMCVPR
EMMCVPR
In this paper we propose a variational method to adaptively decompose an image into few different modes of separate spectral bands, which are unknown before. A popular method for recursive one dimensional signal decomposition is the Empirical Mode De
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::484aae0fa6259f39deb873973778aa1e
https://doi.org/10.1007/978-3-319-14612-6_15
https://doi.org/10.1007/978-3-319-14612-6_15