尽管深度卷积神经网络（CNN）在图像超分辨率（SR）中获得了出色的性能，但随着CNN模型变得越来越深，其计算成本在几何上增加。同时，中间层的特征在整个通道上受到同等对待，因此阻碍了CNN的表示能力。..

Attention-Aware Linear Depthwise Convolution for Single Image Super-Resolution

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.