  Although deep convolutional neural networks (CNNs) have obtained outstanding
performance in image superresolution (SR), their computational cost increases
geometrically as CNN models get deeper and wider. Meanwhile, the features of
intermediate layers are treated equally across the channel, thus hindering the
representational capability of CNNs. In this paper, we propose an
attention-aware linear depthwise network to address the problems for single
image SR, named ALDNet. Specifically, linear depthwise convolution allows
CNN-based SR models to preserve useful information for reconstructing a
super-resolved image while reducing computational burden. Furthermore, we
design an attention-aware branch that enhances the representation ability of
depthwise convolution layers by making full use of depthwise filter
interdependency. Experiments on publicly available benchmark datasets show that
ALDNet achieves superior performance to traditional depthwise separable
convolutions in terms of quantitative measurements and visual quality.
