relation: http://eprints.imtlucca.it/2413/
title: Compact and coherent dictionary construction for example-based super-resolution
creator: Bevilacqua, Marco
creator: Roumy, Aline
creator: Guillemot, Christine
creator: Alberi-Morel, Marie Line
subject: QA75 Electronic computers. Computer science
description: 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.
publisher: IEEE
date: 2013-05
type: Book Section
type: PeerReviewed
identifier:   Bevilacqua, Marco and Roumy, Aline and Guillemot, Christine and Alberi-Morel, Marie Line  Compact and coherent dictionary construction for example-based super-resolution.   In:  Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).   IEEE, pp. 2222-2226.       (2013)  
relation: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6638049&isnumber=6637585
relation: 10.1109/ICASSP.2013.6638049