  In this paper, we propose a new joint dictionary learning method for
example-based image super-resolution (SR), using sparse representation. The
low-resolution (LR) dictionary is trained from a set of LR sample image
patches. Using the sparse representation coefficients of these LR patches over
the LR dictionary, the high-resolution (HR) dictionary is trained by minimizing
the reconstruction error of HR sample patches. The error criterion used here is
the mean square error. In this way we guarantee that the HR patches have the
same sparse representation over HR dictionary as the LR patches over the LR
dictionary, and at the same time, these sparse representations can well
reconstruct the HR patches. Simulation results show the effectiveness of our
method compared to the state-of-art SR algorithms.
