Abstract In many recent image super-resolution (SR) methods based on convolutional neural networks (CNNs), the superior performance was achieved by training very large networks, which may not be suitable for real-world applications with limited computing resources. Therefore, it is necessary to develop more compact networks that achieve a better trade-off between the model size and the performance. In this paper, we propose an efficient and effective network called multi-path wide-activated residual network (MWRN). Firstly, as the basic building block of MWRN, the multi-path wide-activated residual block (MWRB) is presented to extract the multi-scale features. MWRB consists of three parallel wide-activated residual paths, where the dilated convolutions with different dilation factors are used to increase the receptive fields. Secondly, the fusional channel attention (FCA) module, which contains a bottleneck layer and a multi-path wide-activated residual channel attention (MWRCA) block, is designed to well exploit the multi-level features in MWRN. In each FCA, the MWRCA block refines the fused features by taking the interdependencies among feature channels into consideration. The experiments demonstrate that, compared with the state-of-the-art methods, the proposed MWRN model is able to provide very competitive performance with a relatively small number of parameters.

中文翻译：

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使用多路径宽激活残差网络的精确单幅图像超分辨率

摘要 在最近许多基于卷积神经网络 (CNN) 的图像超分辨率 (SR) 方法中，卓越的性能是通过训练非常大的网络来实现的，这可能不适用于计算资源有限的实际应用。因此，有必要开发更紧凑的网络，在模型大小和性能之间实现更好的权衡。在本文中，我们提出了一种称为多路径宽激活残差网络（MWRN）的高效网络。首先，作为MWRN的基本构建块，提出了多路径宽激活残差块（MWRB）来提取多尺度特征。MWRB 由三个平行的宽激活残差路径组成，其中具有不同扩张因子的扩张卷积用于增加感受野。第二，融合通道注意 (FCA) 模块包含一个瓶颈层和一个多路径宽激活剩余通道注意 (MWRCA) 块，旨在很好地利用 MWRN 中的多级特征。在每个 FCA 中，MWRCA 块通过考虑特征通道之间的相互依赖性来细化融合特征。实验表明，与最先进的方法相比，所提出的 MWRN 模型能够以相对较少的参数提供非常有竞争力的性能。MWRCA 模块通过考虑特征通道之间的相互依赖性来细化融合特征。实验表明，与最先进的方法相比，所提出的 MWRN 模型能够以相对较少的参数提供非常有竞争力的性能。MWRCA 模块通过考虑特征通道之间的相互依赖性来细化融合特征。实验表明，与最先进的方法相比，所提出的 MWRN 模型能够以相对较少的参数提供非常有竞争力的性能。