Attention-Aware Linear Depthwise Convolution for Single Image Super-Resolution
Seong-min Hwang, Juhwan Lee, Cheolkon Jung, Jin‐Young Kim · 2020
Although deep convolutional neural networks (CNNs) have obtained outstanding performance in image super-resolution (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 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 to enhance the representation ability of depthwise convolution layers by making full use of depthwise filter interdependencies. 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.