Dual Residual Networks Leveraging the Potential of Paired Operations for Image Restoration

Understand

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.