Contextual Dictionary Learning for Super Resolution
Yu We · 2014
This paper proposed a novel dictionary learning method for single image super resolution based on sparse representation.We tried to utilize patch-level clustering to enhance the contextual information in atom learning stage.Unlike the previous dictionary learning works using the image classification,our training set is constructed from the high-resolution and low-resolution patch pairs labeled by different patch-level class,which is more appropriate for image reconstruction.This approach tried to promote the transfer ability of the dictionary which is built on a limited training set and can eliminate the atoms redundancy introduced by multiple training subsets.