  Recently, machine learning based single image super resolution (SR)
approaches focus on jointly learning representations for high-resolution (HR)
and low-resolution (LR) image patch pairs to improve the quality of the
super-resolved images. However, due to treat all image pixels equally without
considering the salient structures, these approaches usually fail to produce
visual pleasant images with sharp edges and fine details. To address this
issue, in this work we present a new novel SR approach, which replaces the main
building blocks of the classical interpolation pipeline by a flexible,
content-adaptive deep neural networks. In particular, two well-designed
structure-aware components, respectively capturing local- and holistic- image
contents, are naturally incorporated into the fully-convolutional
representation learning to enhance the image sharpness and naturalness.
Extensively evaluations on several standard benchmarks (e.g., Set5, Set14 and
BSD200) demonstrate that our approach can achieve superior results, especially
on the image with salient structures, over many existing state-of-the-art SR
methods under both quantitative and qualitative measures.
