In this paper, we propose an enhanced dual path attention network (EDPAN) for image super-resolution. ResNet is good at implicitly reusing extracted features, DenseNet is good at exploring new features. Dual Path Network (DPN) combines ResNets and DenseNet to create a more accurate architecture than the straightforward one. We experimentally show that the residual network performs best when each block consists of two convolutions, and the dense network performs best when each micro-block consists of one convolution. Following these ideas, our EDPAN exploits the advantages of the residual structure and the dense structure. Besides, to deploy the computations for features more effectively, we introduce the attention mechanism into our EDPAN. Moreover, to relieve the parameters burden, we also utilize recursive learning to propose a lightweight model. In the experiments, we demonstrate the effectiveness and robustness of our proposed EDPAN on different degradation situations. The quantitative results and visualization comparison can sufficiently indicate that our EDPAN achieves favorable performance over the state-of-the-art frameworks.

中文翻译：

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EDP​​AN：用于单图像超分辨率的增强型双路径注意网络

在本文中，我们提出了一种用于图像超分辨率的增强型双路径注意力网络（EDPAN）。ResNet 擅长隐式重用提取的特征，DenseNet 擅长探索新特征。双路径网络 (DPN) 结合了 ResNet 和 DenseNet，创建了一种比简单的架构更准确的架构。我们通过实验表明，当每个块由两个卷积组成时，残差网络表现最好，而当每个微块由一个卷积组成时，密集网络表现最好。遵循这些想法，我们的 EDPAN 利用了残差结构和密集结构的优势。此外，为了更有效地部署特征计算，我们将注意力机制引入我们的 EDPAN。此外，为了减轻参数负担，我们还利用递归学习提出了一个轻量级模型。在实验中，我们证明了我们提出的 EDPAN 在不同退化情况下的有效性和鲁棒性。定量结果和可视化比较可以充分表明我们的 EDPAN 在最先进的框架上取得了良好的性能。