  Dictionary learning can be used for image superresolution by learning a pair
of coupled dictionaries of image patches from high-resolution and
low-resolution image pairs such that the corresponding pairs share the same
sparse vector when represented by the coupled dictionaries. These dictionaries
then can be used to to reconstruct the corresponding high-resolution patches
from low-resolution input images based on sparse recovery. The idea is to
recover the shared sparse vector using the low-resolution dictionary and then
multiply it by the high-resolution dictionary to recover the corresponding
high-resolution image patch. In this work, we study the effect of the sparse
recovery algorithm that we use on the quality of the reconstructed images. We
offer empirical experiments to search for the best sparse recovery algorithm
that can be used for this purpose.
