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» On Sparsity and Overcompleteness in Image Models
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CVPR
2009
IEEE
13 years 11 months ago
High-quality curvelet-based motion deblurring from an image pair
One promising approach to remove motion deblurring is to recover one clear image using an image pair. Existing dual-image methods require an accurate image alignment between the i...
Jian-Feng Cai, Hui Ji, Chaoqiang Liu, Zuowei Shen
PAMI
2012
11 years 10 months ago
Face Recognition Using Sparse Approximated Nearest Points between Image Sets
—We propose an efficient and robust solution for image set classification. A joint representation of an image set is proposed which includes the image samples of the set and thei...
Yiqun Hu, Ajmal S. Mian, Robyn A. Owens
IDA
2009
Springer
14 years 2 months ago
Estimating Markov Random Field Potentials for Natural Images
Markov Random Field (MRF) models with potentials learned from the data have recently received attention for learning the low-level structure of natural images. A MRF provides a pri...
Urs Köster, Jussi T. Lindgren, Aapo Hyvä...
TIP
2010
127views more  TIP 2010»
13 years 6 months ago
Bayesian Compressive Sensing Using Laplace Priors
In this paper we model the components of the compressive sensing (CS) problem, i.e., the signal acquisition process, the unknown signal coefficients and the model parameters for ...
S. Derin Babacan, Rafael Molina, Aggelos K. Katsag...
ICASSP
2011
IEEE
12 years 11 months ago
Sparse coding and dictionary learning based on the MDL principle
The power of sparse signal coding with learned overcomplete dictionaries has been demonstrated in a variety of applications and fields, from signal processing to statistical infe...
Ignacio Ramírez, Guillermo Sapiro