Dual residual attention module network for single image super resolution

Abstract

Recent studies show that research on single image super-resolution (SISR) has achieved great success by using deep convolutional neural network (CNN). Different types of features obtained in deep CNN have different contribution. However, most of the previous models ignore the distinction between different features and deal with them in the same way, which affects the representational capacity of the models. On the other hand, receptive fields with different size capture diverse features from the input. Based on the above considerations, we propose a dual residual attention module (DRAM) network which concentrates on recovering the high-frequency details and sharing the information between two receptive fields of different sizes. We construct local information integration (LFI) module as the basic module to make full use of the local information. The LFI module is a cascade of several dual residual attention fusion (DRAF) blocks with a dense connection structure. The feature modulation can focus on important features and suppress unimportant ones. The evaluation results on five benchmark datasets demonstrate the superiority of our DRAM network against the state-of-the-art algorithms.