Single Image Super-Resolution via Residual Neuron Attention Networks
This work addresses the problem of efficient and effective image super-resolution for computer vision applications, but it is incremental as it builds on existing CNN-based methods.
The paper tackles single image super-resolution by proposing Residual Neuron Attention Networks (RNAN), which integrates Global Context-enhanced Residual Groups with a Residual Neuron Attention mechanism to improve feature representation, achieving results comparable to state-of-the-art methods with a simplified architecture.
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.