
Proceedings Paper
Tight frames for multiscale and multidirectional image analysisFormat | Member Price | Non-Member Price |
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Paper Abstract
We propose a framework for analyzing and visualizing data at multiple scales and directions by constructing a novel class of tight frames. We describe an elegant way of creating 2D tight frames from 1D sets of orthonormal vectors and show how to exploit the representation redundancy in a computationally efficient manner. Finally, we employ this framework to perform image superresolution via edge detection and characterization.
Paper Details
Date Published: 29 May 2013
PDF: 16 pages
Proc. SPIE 8750, Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering XI, 875004 (29 May 2013); doi: 10.1117/12.2016474
Published in SPIE Proceedings Vol. 8750:
Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering XI
Harold H. Szu, Editor(s)
PDF: 16 pages
Proc. SPIE 8750, Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering XI, 875004 (29 May 2013); doi: 10.1117/12.2016474
Show Author Affiliations
Edward H. Bosch, National Geospatial-Intelligence Agency (United States)
Alexey Castrodad, National Geospatial-Intelligence Agency (United States)
John S. Cooper, National Geospatial-Intelligence Agency (United States)
Alexey Castrodad, National Geospatial-Intelligence Agency (United States)
John S. Cooper, National Geospatial-Intelligence Agency (United States)
Wojtek Czaja, Univ. of Maryland (United States)
Julia Dobrosotskaya, Univ. of Maryland (United States)
Julia Dobrosotskaya, Univ. of Maryland (United States)
Published in SPIE Proceedings Vol. 8750:
Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering XI
Harold H. Szu, Editor(s)
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