  Recently, deep neural networks have achieved impressive performance in terms
of both reconstruction accuracy and efficiency for single image
super-resolution (SISR). However, the network model of these methods is a fully
convolutional neural network, which is limit to exploit the differentiated
contextual information over the global region of the input image because of the
weight sharing in convolution height and width extent. In this paper, we
discuss a new SISR architecture where features are extracted in the
low-resolution (LR) space, and then we use a fully connected layer which learns
an array of differentiated upsampling weights to reconstruct the desired
high-resolution (HR) image from the final obtained LR features. By doing so, we
effectively exploit the differentiated contextual information over the whole
input image region, whilst maintaining the low computational complexity for the
overall SR operations. In addition, we introduce an edge difference constraint
into our loss function to preserve edges and texture structures. Extensive
experiments validate that our SISR method outperforms the existing
state-of-the-art methods.
