Pyramid Fusion Attention Network For Single Image Super-Resolution

Abstract

Recently, convolutional neural network (CNN) has made a mighty advance in image super-resolution (SR). Most recent models exploit attention mechanism (AM) to focus on high-frequency information. However, these methods exclusively consider interdependencies among channels or spatials, leading to equal treatment of channel-wise or spatial-wise features thus hindering the power of AM. In this paper, we propose a pyramid fusion attention network (PFAN) to tackle this problem. Specifically, a novel pyramid fusion attention (PFA) is developed where stacked residual blocks are employed to model the relationship between pixels among all channels, and pyramid fusion structure is adopted to expand receptive field. Besides, a progressive backward fusion strat-egy is introduced to make full use of hierarchical features, which are beneficial to obtaining more contextual representations. Comprehensive experiments demonstrate the superiority of our proposed PFAN against state-of-the-art methods.