  Single image super-resolution (SR) aims to estimate a high-resolution (HR)
image from a lowresolution (LR) input. Image priors are commonly learned to
regularize the otherwise seriously ill-posed SR problem, either using external
LR-HR pairs or internal similar patterns. We propose joint SR to adaptively
combine the advantages of both external and internal SR methods. We define two
loss functions using sparse coding based external examples, and epitomic
matching based on internal examples, as well as a corresponding adaptive weight
to automatically balance their contributions according to their reconstruction
errors. Extensive SR results demonstrate the effectiveness of the proposed
method over the existing state-of-the-art methods, and is also verified by our
subjective evaluation study.
