Lightweight image super-resolution with multiscale residual attention network

In recent years, various convolutional neural networks have successfully applied to single-image super-resolution task. However, most existing models with deeper or wider networks require heavy computation and memory consumption that restrict them in practice. To solve the above questions, we propose a lightweight multiscale residual attention network, which not merely can extract more detail to improve the quality of the image but also decrease the usage of the parameters. More specifically, a multiscale residual attention block (MRAB) as the basic unit can fully exploit the image features with different sizes of convolutional kernels. Meanwhile, the attention mechanism can be adaptive to recalibrate channel and spatial information of feature mappings. Furthermore, a local information integration module (LFIM) is designed as the network architecture to maximize the use of local information. The LFIM consists of several MRAB and a local skip connection to complement information loss. Our experimental results show that our method is superior to the representative algorithms in performance with fewer parameters and computational overhead.