Super-Resolution convolutional neural networks have recently demonstrated high-quality restoration for single images. However, existing algorithms often require very deep architectures and long training times. Furthermore, current convolutional neural networks for super-resolution are unable to exploit features at multiple scales and weigh them equally or at only static scale only, limiting their learning capability. In this exposition, we present a compact and accurate super-resolution algorithm, namely, densely residual laplacian network (DRLN). The proposed network employs cascading residual on the residual structure to allow the flow of low-frequency information to focus on learning high and mid-level features. In addition, deep supervision is achieved via the densely concatenated residual blocks settings, which also helps in learning from high-level complex features. Moreover, we propose Laplacian attention to model the crucial features to learn the inter and intra-level dependencies between the feature maps. Furthermore, comprehensive quantitative and qualitative evaluations on low-resolution, noisy low-resolution, and real historical image benchmark datasets illustrate that our DRLN algorithm performs favorably against the state-of-the-art methods visually and accurately.

中文翻译：

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密集残差拉普拉斯超分辨率


超分辨率卷积神经网络最近证明了单图像的高质量恢复。然而，现有算法通常需要非常深的架构和较长的训练时间。此外，当前用于超分辨率的卷积神经网络无法利用多个尺度的特征并对它们进行同等或仅静态尺度的加权，从而限制了它们的学习能力。在本次阐述中，我们提出了一种紧凑而准确的超分辨率算法，即密集残差拉普拉斯网络（DRLN）。所提出的网络在残差结构上采用级联残差，使低频信息流集中于学习高中级特征。此外，深度监督是通过密集连接的残差块设置实现的，这也有助于从高级复杂特征中学习。此外，我们提出拉普拉斯注意力模型来对关键特征进行建模，以学习特征图之间的层间和层内依赖性。此外，对低分辨率、噪声低分辨率和真实历史图像基准数据集的全面定量和定性评估表明，我们的 DRLN 算法在视觉上和准确度上优于最先进的方法。