TY  - CHAP
ID  - eprints2411
N2  - This paper describes a novel method for single-image super-resolution (SR) based on a neighbor embedding technique which uses Semi-Nonnegative Matrix Factorization (SNMF). Each low-resolution (LR) input patch is approximated by a linear combination of nearest neighbors taken from a dictionary. This dictionary stores low-resolution and corresponding high-resolution (HR) patches taken from natural images and is thus used to infer the HR details of the super-resolved image. The entire neighbor embedding procedure is carried out in a feature space. Features which are either the gradient values of the pixels or the mean-subtracted luminance values are extracted from the LR input patches, and from the LR and HR patches stored in the dictionary. The algorithm thus searches for the K nearest neighbors of the feature vector of the LR input patch and then computes the weights for approximating the input feature vector. The use of SNMF for computing the weights of the linear approximation is shown to have a more stable behavior than the use of LLE and lead to significantly higher PSNR values for the super-resolved images.
SP  - 1289
KW  - Semi-nonnegative Matrix Factorization; Super-resolution; neighbor embedding
SN  - 978-1-4673-0044-5 
AV  - none
N1  - IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Kyoto, Japan, 25-30 March 2012 
EP  - 1292
T2  - Proceedings of  IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
UR  - http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6288125&isnumber=6287775
PB  - IEEE
A1  - Bevilacqua, Marco
A1  - Roumy, Aline
A1  - Guillemot, Christine
A1  - Alberi-Morel, Marie Line
TI  - Neighbor embedding based single-image super-resolution using Semi-Nonnegative Matrix Factorization
Y1  - 2012/03//
ER  -