基于残差神经元注意力网络的单幅图像超分辨率
Single Image Super-Resolution via Residual Neuron Attention Networks
摘要：
深度卷积神经网络（DCNN）在单图像超分辨率（SISR）方面取得了令人印象深刻的性能。为了进一步提高性能，现有的基于CNN的方法通常侧重于设计更深层次的网络架构。然而，我们认为盲目地增加网络的深度并不是最明智的方式。在本文中，我们提出了一种新的端到端的残余神经元注意力网络（RNAN）更有效和有效的SISR。从结构上讲，我们的RNAN是精心设计的全局上下文增强残差组（GCRG）的顺序集成，它从粗到细提取超分辨特征。我们的GCRG设计有两个创新点。首先，在GCRG的每个块中提出了残余神经元注意（RNA）机制，以揭示神经元的相关性，以更好地表示特征。此外，全局上下文（GC）块被嵌入到RNAN在每个GCRG的末尾，有效地建模的全局上下文信息。实验结果表明，我们的RNAN实现了与国家的最先进的方法在定量指标和视觉质量方面的可比结果，但是，与简化的网络架构。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
