  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 real-world 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.
