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Compact and coherent dictionary construction for example-based super-resolution

Abstract : 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.
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Submitted on : Wednesday, October 23, 2013 - 12:29:03 PM
Last modification on : Friday, October 8, 2021 - 6:50:16 PM
Long-term archiving on: : Friday, January 24, 2014 - 4:25:13 AM


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  • HAL Id : hal-00875964, version 1


Marco Bevilacqua, Aline Roumy, Christine Guillemot, Marie-Line Alberi Morel. Compact and coherent dictionary construction for example-based super-resolution. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), May 2013, Vancouver, Canada. ⟨hal-00875964⟩



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