Zobrazeno 1 - 10
of 18
pro vyhledávání: '"Julia Dobrosotskaya"'
Autor:
Martin Ehler, Julia Dobrosotskaya, Denise Cunningham, Wai T Wong, Emily Y Chew, Wojtek Czaja, Robert F Bonner
Publikováno v:
PLoS ONE, Vol 10, Iss 7, p e0131881 (2015)
We introduce and describe a novel non-invasive in-vivo method for mapping local rod rhodopsin distribution in the human retina over a 30-degree field. Our approach is based on analyzing the brightening of detected lipofuscin autofluorescence within s
Externí odkaz:
https://doaj.org/article/0fef2e5feed54d24a114f96b4096327c
Autor:
Julia Dobrosotskaya, Weihong Guo
Publikováno v:
Journal of Imaging, Vol 3, Iss 3, p 26 (2017)
We introduce a variational model for multi-phase image segmentation that uses a multiscale sparse representation frame (wavelets or other) in a modified diffuse interface context. The segmentation model we present differs from other state-of-the-art
Externí odkaz:
https://doaj.org/article/287e78db52f04eadae3dca49533cef9c
Publikováno v:
AIAA SCITECH 2023 Forum.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f89c249ebe9dd7d12d64677af9b4e9a3
https://doi.org/10.2514/6.2023-2439
https://doi.org/10.2514/6.2023-2439
Autor:
Julia Dobrosotskaya, Weihong Guo
Publikováno v:
Wavelets and Sparsity XVIII.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::88572d3fb0b97247efc074969c4d4c09
https://doi.org/10.1117/12.2529695
https://doi.org/10.1117/12.2529695
Publikováno v:
GEM - International Journal on Geomathematics. 7:275-297
With the emergence of new remote sensing modalities, it becomes increasingly important to find novel algorithms for fusion and integration of different types of data for the purpose of improving performance of applications, such as target/anomaly det
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f84fc4f954baceac767c1d71365bd328
https://doi.org/10.1007/s13137-016-0085-0
https://doi.org/10.1007/s13137-016-0085-0
Publikováno v:
SIAM Journal on Imaging Sciences. 6:698-729
A wavelet analogue of the Ginzburg--Landau energy was recently designed and integrated in variational methods for image processing. In this paper we prove global well-posedness of the gradient descent equation (in the weak sense) and convergence to a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::961834ea9c34513f40b9f868e215b019
https://doi.org/10.1137/100812859
https://doi.org/10.1137/100812859
Publikováno v:
Interfaces and Free Boundaries. :497-525
Fourier analysis provides many elegant approaches to differential operators and related tools in PDE-based image processing. Our work develops the idea of using a more localized basis than the Fourier one in the context of variational methods based o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a2cb48c026de11bd9359d10f95c809c3
https://doi.org/10.4171/ifb/243
https://doi.org/10.4171/ifb/243
Publikováno v:
IEEE Transactions on Image Processing. 17:657-663
We construct a new variational method for blind deconvolution of images and inpainting, motivated by recent PDE-based techniques involving the Ginzburg-Landau functional, but using more localized wavelet-based methods. We present results for both bin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ee61728c3831bd11a90057990d64fb22
https://doi.org/10.1109/tip.2008.919367
https://doi.org/10.1109/tip.2008.919367
Autor:
Julia Dobrosotskaya, Weihong Guo
Publikováno v:
SPIE Proceedings.
We introduce a PDE-free variational model for multiphase image segmentation that uses a sparse representation basis (wavelets or other) instead of a Fourier basis in a modified diffuse interface context. The segmentation model we present differs from
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::864f8dbfad6b2723ad55dfdaf5eb385b
https://doi.org/10.1117/12.2189147
https://doi.org/10.1117/12.2189147
Publikováno v:
SPIE Proceedings.
We shall introduce a novel methodology for data reconstruction and recovery, based on composite wavelet representations. These representations include shearlets and crystallographic wavelets, among others, and they allow for an increased directional
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::01ca3cea7358fb8f0197492123123e1a
https://doi.org/10.1117/12.2019219
https://doi.org/10.1117/12.2019219