Dual Residual Networks Leveraging the Potential of Paired Operationsfor Image Restoration
Abstract
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., upand down-sampling or convolution with large- and smallsize 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.