Recently, a very deep convolutional neural network (CNN) has achieved impressive results in image super-resolution (SR). In particular, residual learning techniques are widely used. However, the previously proposed residual block can only extract one single-level semantic feature maps of one single receptive field. Therefore, it is necessary to stack the residual blocks to extract higher-level semantic feature maps, which will significantly deepen the network. While a very deep network is hard to train and limits the representation for reconstructing the hierarchical information. Based on the residual block, we propose an enhanced multi-scale residual network (EMRN) to take advantage of hierarchical image features via dense connected enhanced multi-scale residual blocks (EMRBs). Specifically, the newly proposed residual block (EMRB) is capable of constructing multi-level semantic feature maps by a two-branch inception. The two-branch inception in our proposed EMRB consists of 2 convolutional layers and 4 convolutional layers in each branch respectively, therefore we have different ranges of receptive fields within one single EMRB. Meanwhile, the local feature fusion (LFF) is used in every EMRB to adaptively fuse the local feature maps extracted by the two-branch inception. Furthermore, global feature fusion (GFF) in EMRN is then used to obtain abundant useful features from previous EMRBs and subsequent ones in a holistic manner. Experiments on benchmark datasets suggest that our EMRN performs favorably over the state-of-the-art methods in reconstructing further superior super-resolution (SR) images.

最近，非常深的卷积神经网络（CNN）在图像超分辨率（SR）方面取得了令人印象深刻的结果。特别地，残差学习技术被广泛使用。然而，先前提出的残差块只能提取一个接收域的一个单层语义特征图。因此，有必要堆叠剩余的块以提取更高级别的语义特征图，这将大大加深网络。虽然很难训练非常深的网络，但它限制了用于重建层次信息的表示形式。基于残差块，我们提出了一种增强的多尺度残差网络（EMRN），以通过密集连接的增强型多尺度残差块（EMRB）来利用分层图像特征。具体来说，新提出的残差块（EMRB）能够通过两个分支开始构造多层语义特征图。我们提出的EMRB中的两分支起始由分别在每个分支中的2个卷积层和4个卷积层组成，因此我们在单个EMRB中具有不同范围的接收场。同时，在每个EMRB中使用局部特征融合（LFF）来自适应融合通过两分支起始提取的局部特征图。此外，然后使用EMRN中的全局特征融合（GFF）以整体方式从先前的EMRB和后续的EMRB中获得大量有用的特征。在基准数据集上进行的实验表明，我们的EMRN在重建其他更高级的超分辨率（SR）图像方面表现优于最新技术。