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» Optimal Approximation of Signal Priors
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ICCV
2011
IEEE
12 years 9 months ago
From Learning Models of Natural Image Patches to Whole Image Restoration
Learning good image priors is of utmost importance for the study of vision, computer vision and image processing applications. Learning priors and optimizing over whole images can...
Daniel Zoran, Yair Weiss
TOG
2010
153views more  TOG 2010»
13 years 3 months ago
Content-adaptive parallax barriers: optimizing dual-layer 3D displays using low-rank light field factorization
We optimize automultiscopic displays built by stacking a pair of modified LCD panels. To date, such dual-stacked LCDs have used heuristic parallax barriers for view-dependent imag...
Douglas Lanman, Matthew Hirsch, Yunhee Kim, Ramesh...
JMM2
2007
96views more  JMM2 2007»
13 years 8 months ago
A Framework for Linear Transform Approximation Using Orthogonal Basis Projection
—This paper aims to develop a novel framework to systematically trade-off computational complexity with output distortion in linear multimedia transforms, in an optimal manner. T...
Yinpeng Chen, Hari Sundaram
TASLP
2010
142views more  TASLP 2010»
13 years 3 months ago
Beyond the Narrowband Approximation: Wideband Convex Methods for Under-Determined Reverberant Audio Source Separation
We consider the problem of extracting the source signals from an under-determined convolutive mixture assuming known mixing filters. State-of-the-art methods operate in the time-fr...
M. Kowalski, Emmanuel Vincent, Rémi Gribonv...
ICASSP
2009
IEEE
14 years 3 months ago
Map approach to learning sparse Gaussian Markov networks
Recently proposed l1-regularized maximum-likelihood optimization methods for learning sparse Markov networks result into convex problems that can be solved optimally and efficien...
Narges Bani Asadi, Irina Rish, Katya Scheinberg, D...