With the success of the deep residual network for image recognition tasks, the residual connection or skip connection has been widely used in deep learning models for various vision tasks, including single image super-resolution (SISR). Most existing SISR approaches pay particular attention to residual learning, while few studies investigate highway connection for SISR. Although skip connection can help to alleviate the vanishing gradient problem and enable fast training of the deep network, it still provides the coarse level of approximation in both forward and backward propagation paths and thus challenging to recover high-frequency details. To address this issue, we propose a novel model for SISR by using highway connection (HNSR), which composes of a nonlinear gating mechanism to further regulate the information. By using the global residual learning and replacing all local residual learning with designed gate unit in highway connection, HNSR has the capability of efficiently learning different hierarchical features and recovering much more details in image reconstruction. The experimental results have validated that HNSR can provide not only improved quality but also less prone to a few common problems during training. Besides, the more robust and efficient model is suitable for implementation in real-time and mobile systems.

随着用于图像识别任务的深度残差网络的成功应用，残差连接或跳过连接已广泛用于各种视觉任务的深度学习模型中，包括单图像超分辨率（SISR）。现有的大多数SISR方法都特别关注残差学习，而很少有研究针对SISR研究公路连接。尽管跳过连接可以帮助缓解消失的梯度问题并实现对深层网络的快速训练，但它仍可以在前向和后向传播路径中提供近似的近似值，因此难以恢复高频细节。为了解决这个问题，我们提出了一种使用公路连接（HNSR）的SISR新模型，该模型由非线性门控机制组成，可以进一步调节信息。通过使用全局残差学习并将所有局部残差学习替换为高速公路连接中的设计门单元，HNSR能够有效地学习不同的层次特征并在图像重建中恢复更多细节。实验结果证明，HNSR不仅可以提高质量，而且在训练过程中不易出现一些常见问题。此外，更健壮和高效的模型适合在实时和移动系统中实施。实验结果证明，HNSR不仅可以提高质量，而且在训练过程中不易出现一些常见问题。此外，更健壮和高效的模型适合在实时和移动系统中实施。实验结果证明，HNSR不仅可以提高质量，而且在训练过程中不易出现一些常见问题。此外，更健壮和高效的模型适合在实时和移动系统中实施。