Dual-attention guided multi-scale network for single image super-resolution

Deep convolutional neural networks (DCNNs) have been employed in single image super-resolution (SISR) tasks and achieved considerable performance. However, most of the existing approaches simply deepen and widen the network structure to increase the receptive field of the convolution kernel and adopt the equal processing methods in the channel and the spatial dimension, which brings about the rise of computational complexity and the loss of important information. To address these issues, we propose dual-attention guided multi-scale network (DAMSN) for SISR, which intends to obtain better performance with relatively fewer parameters. Exactly, we first introduce a non-local residual block (NLRB) to capture the abundant information of both low-level features and high-level features. Furthermore, we design a multi-scale dual attention block (MSDAB) to diminish the redundant information and extract features from different scales powerfully. Inside the MSDAB, dual attention module (DAM) focuses on high-frequency information extraction by combining the channel and spatial mechanism. Followed by DAM, multi-scale residual module (MSRM) is utilized to learn the informative feature via multi-sized convolution kernels adaptively. Extensive experiments demonstrate that our proposed DAMSN is superior to other state-of-the-art approaches in terms of both quantitative metrics and subjective visual quality.