In this paper, we study design of deep neural networks for tasks of image restoration. We propose a novel style of residual connections dubbed "dual residual connection", which exploits the potential of paired operations, e.g., up- and down-sampling or convolution with large- and small-size kernels. We design a modular block implementing this connection style; it is equipped with two containers to which arbitrary paired operations are inserted. Adopting the "unraveled" view of the residual networks proposed by Veit et al., we point out that a stack of the proposed modular blocks allows the first operation in a block interact with the second operation in any subsequent blocks. Specifying the two operations in each of the stacked blocks, we build a complete network for each individual task of image restoration. We experimentally evaluate the proposed approach on five image restoration tasks using nine datasets. The results show that the proposed networks with properly chosen paired operations outperform previous methods on almost all of the tasks and datasets.

本文研究图像恢复任务的深度神经网络设计，提出了一种新颖的双残差连接风格，构建了具有双操作容器的模块化块，并采用 Veit 等提出的残差网络 '展开' 视图，构建完整的网络用于各个任务；实验结果表明，采用适当选择的操作的此方法在几乎所有任务和数据集上的表现优于以前的方法。

双重残差网络利用配对操作的潜力进行图像恢复