Abstract. In recent years, deep learning methods have been successfully applied to single-image super-resolution tasks. Despite their great
performances, deep learning methods cannot be easily applied to realworld applications due to the requirement of heavy computation. In this
paper, we address this issue by proposing an accurate and lightweight
deep network for image super-resolution. In detail, we design an architecture that implements a cascading mechanism upon a residual network.
We also present variant models of the proposed cascading residual network to further improve efficiency. Our extensive experiments show that
even with much fewer parameters and operations, our models achieve
performance comparable to that of state-of-the-art methods.