Deep convolutional neural networks (CNNs) have contributed to the significant progress of the single-image super-resolution (SISR) field. However, the majority of existing CNN-based models maintain high performance with massive parameters and exceedingly deeper structures. Moreover, several algorithms essentially have underused the low-level features, thus causing relatively low performance. In this article, we address these problems by exploring two strategies based on novel local wider residual blocks (LWRBs) to effectively extract the image features for SISR. We propose a cascading residual network (CRN) that contains several locally sharing groups (LSGs), in which the cascading mechanism not only promotes the propagation of features and the gradient but also eases the model training. Besides, we present another enhanced residual network (ERN) for image resolution enhancement. ERN employs a dual global pathway structure that incorporates nonlocal operations to catch long-distance spatial features from the the original low-resolution (LR) input. To obtain the feature representation of the input at different scales, we further introduce a multiscale block (MSB) to directly detect low-level features from the LR image. The experimental results on four benchmark datasets have demonstrated that our models outperform most of the advanced methods while still retaining a reasonable number of parameters.

中文翻译：

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用于精确单图像超分辨率的级联和增强残差网络


深度卷积神经网络（CNN）为单图像超分辨率（SISR）领域的重大进展做出了贡献。然而，大多数现有的基于 CNN 的模型都保持着具有大量参数和极深结构的高性能。此外，一些算法本质上没有充分利用低级特征，从而导致性能相对较低。在本文中，我们通过探索两种基于新颖的局部更宽残差块（LWRB）的策略来解决这些问题，以有效地提取 SISR 的图像特征。我们提出了一种包含多个本地共享组（LSG）的级联残差网络（CRN），其中级联机制不仅促进了特征和梯度的传播，而且还简化了模型训练。此外，我们提出了另一种用于图像分辨率增强的增强残差网络（ERN）。 ERN 采用双全局路径结构，其中包含非局部操作，以从原始低分辨率 (LR) 输入中捕获长距离空间特征。为了获得不同尺度下输入的特征表示，我们进一步引入了多尺度块（MSB）来直接从 LR 图像中检测低级特征。四个基准数据集的实验结果表明，我们的模型优于大多数先进方法，同时仍然保留了合理数量的参数。