  The recent progress of deep convolutional neural networks has enabled great
success in single image super-resolution (SISR) and many other vision tasks.
Their performances are also being increased by deepening the networks and
developing more sophisticated network structures. However, finding an optimal
structure for the given problem is a difficult task, even for human experts.
For this reason, neural architecture search (NAS) methods have been introduced,
which automate the procedure of constructing the structures. In this paper, we
expand the NAS to the super-resolution domain and find a lightweight densely
connected network named DeCoNASNet. We use a hierarchical search strategy to
find the best connection with local and global features. In this process, we
define a complexity-based penalty for solving image super-resolution, which can
be considered a multi-objective problem. Experiments show that our DeCoNASNet
outperforms the state-of-the-art lightweight super-resolution networks designed
by handcraft methods and existing NAS-based design.
