  Deep Convolutional Neural Networks (DCNNs) have achieved impressive
performance in Single Image Super-Resolution (SISR). To further improve the
performance, existing CNN-based methods generally focus on designing deeper
architecture of the network. However, we argue blindly increasing network's
depth is not the most sensible way. In this paper, we propose a novel
end-to-end Residual Neuron Attention Networks (RNAN) for more efficient and
effective SISR. Structurally, our RNAN is a sequential integration of the
well-designed Global Context-enhanced Residual Groups (GCRGs), which extracts
super-resolved features from coarse to fine. Our GCRG is designed with two
novelties. Firstly, the Residual Neuron Attention (RNA) mechanism is proposed
in each block of GCRG to reveal the relevance of neurons for better feature
representation. Furthermore, the Global Context (GC) block is embedded into
RNAN at the end of each GCRG for effectively modeling the global contextual
information. Experiments results demonstrate that our RNAN achieves the
comparable results with state-of-the-art methods in terms of both quantitative
metrics and visual quality, however, with simplified network architecture.
