Single-image super-resolution with multilevel residual attention network

Recently, a great variety of image super-resolution (SR) algorithms based on convolutional neural network (CNN) have been proposed and achieved significant improvement. But how to restore more high-frequency details such as edges and textures is still an unsolved issue. The low-frequency information is similar in a pair of low-resolution and high-resolution images. So the SR model is supposed to pay more attention to the high-frequency features to restore more realistic images. But most CNN-based methods don't consider the different types of features and think the features in different channels and regions contribute equally to the reconstruction performance, which limits the representation capacity of the model. In the meantime, most of these deep networks only simply stack blocks like residual block, which only capture the local features. In this paper, we propose a deep multilevel residual attention network (MRAN) for image SR to focus on the high-frequency features and improve the flow of information. Specially, we propose a channel-wise attention module and a spatial attention module to rescale the channel-wise and spatial weights adaptively, which makes our MRAN focus more on the high-frequency information. Meanwhile, to improve the flow of information and ease the training process, the multilevel residual learning is adopted. Extensive experimental results on five benchmark datasets demonstrate that our MRAN is superior to those state-of-the-art methods for both accuracy and visual comparisons.