TY - GEN
T1 - Nonparametric image interpolation and dictionary learning using spatially-dependent dirichlet and beta process priors
AU - Paisley, John
AU - Zhou, Mingyuan
AU - Sapiro, Guillermo
AU - Carin, Lawrence
N1 - Generated from Scopus record by KAUST IRTS on 2021-02-09
PY - 2010/12/1
Y1 - 2010/12/1
N2 - We present a Bayesian model for image interpolation and dictionary learning that uses two nonparametric priors for sparse signal representations: the beta process and the Dirichlet process. Additionally, the model uses spatial information within the image to encourage sharing of information within image subregions. We derive a hybrid MAP/Gibbs sampler, which performs Gibbs sampling for the latent indicator variables and MAP estimation for all other parameters. We present experimental results, where we show an improvement over other state-of-the-art algorithms in the low-measurement regime. © 2010 IEEE.
AB - We present a Bayesian model for image interpolation and dictionary learning that uses two nonparametric priors for sparse signal representations: the beta process and the Dirichlet process. Additionally, the model uses spatial information within the image to encourage sharing of information within image subregions. We derive a hybrid MAP/Gibbs sampler, which performs Gibbs sampling for the latent indicator variables and MAP estimation for all other parameters. We present experimental results, where we show an improvement over other state-of-the-art algorithms in the low-measurement regime. © 2010 IEEE.
UR - http://ieeexplore.ieee.org/document/5653350/
UR - http://www.scopus.com/inward/record.url?scp=78651074442&partnerID=8YFLogxK
U2 - 10.1109/ICIP.2010.5653350
DO - 10.1109/ICIP.2010.5653350
M3 - Conference contribution
SN - 9781424479948
SP - 1869
EP - 1872
BT - Proceedings - International Conference on Image Processing, ICIP
ER -