  The main challenge of single image super resolution (SISR) is the recovery of
high frequency details such as tiny textures. However, most of the
state-of-the-art methods lack specific modules to identify high frequency
areas, causing the output image to be blurred. We propose an attention-based
approach to give a discrimination between texture areas and smooth areas. After
the positions of high frequency details are located, high frequency
compensation is carried out. This approach can incorporate with previously
proposed SISR networks. By providing high frequency enhancement, better
performance and visual effect are achieved. We also propose our own SISR
network composed of DenseRes blocks. The block provides an effective way to
combine the low level features and high level features. Extensive benchmark
evaluation shows that our proposed method achieves significant improvement over
the state-of-the-art works in SISR.
