TY  - CHAP
PB  - IEEE
A1  - Bevilacqua, Marco
A1  - Roumy, Aline
A1  - Guillemot, Christine
A1  - Alberi-Morel, Marie Line
UR  - http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6638049&isnumber=6637585
Y1  - 2013/05//
KW  - Super-resolution; dictionary learning; example-based;   neighbor embedding
T2  - Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 
N1  - IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , Vancouver, Canada, 26-31 May 2013 
AV  - none
SP  - 2222
TI  - Compact and coherent dictionary construction for example-based super-resolution
N2  - This paper presents a new method to construct a dictionary for example-based super-resolution (SR) algorithms. Example-based SR relies on a dictionary of correspondences of low-resolution (LR) and high-resolution (HR) patches. Having a fixed, prebuilt, dictionary, allows to speed up the SR process; however, in order to perform well in most cases, we need to have big dictionaries with a large variety of patches. Moreover, LR and HR patches often are not coherent, i.e. local LR neighborhoods are not preserved in the HR space. Our designed dictionary learning method takes as input a large dictionary and gives as an output a dictionary with a ?sustainable? size, yet presenting comparable or even better performance. It firstly consists of a partitioning process, done according to a joint k-means procedure, which enforces the coherence between LR and HR patches by discarding those pairs for which we do not find a common cluster. Secondly, the clustered dictionary is used to extract some salient patches that will form the output set.
ID  - eprints2413
EP  - 2226
ER  -