Attention augmented multi-scale network for single image super-resolution

Abstract

Multi-scale convolution can be used in a deep neural network (DNN) to obtain a set of features in parallel with different perceptive fields, which is beneficial to reduce network depth and lower training difficulty. Also, the attention mechanism has great advantages to strengthen representation power of a DNN. In this paper, we propose an attention augmented multi-scale network (AAMN) for single image super-resolution (SISR), in which deep features from different scales are discriminatively aggregated to improve performance. Specifically, the statistics of features at different scales are first computed by global average pooling operation, and then used as a guidance to learn the optimal weight allocation for the subsequent feature recalibration and aggregation. Meanwhile, we adopt feature fusion at two levels to further boost reconstruction power, one of which is intra-group local hierarchical feature fusion (LHFF), and the other is inter-group global hierarchical feature fusion (GHFF). Extensive experiments on public standard datasets indicate the superiority of our AAMN over the state-of-the-art models, in terms of not only quantitative and qualitative evaluation but also model complexity and efficiency.