29 May 2013 Tight frames for multiscale and multidirectional image analysis
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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.
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Edward H. Bosch, Alexey Castrodad, John S. Cooper, Wojtek Czaja, Julia Dobrosotskaya, "Tight frames for multiscale and multidirectional image analysis", Proc. SPIE 8750, Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering XI, 875004 (29 May 2013); doi: 10.1117/12.2016474; https://doi.org/10.1117/12.2016474
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