A novel attention-enhanced network for image super-resolution

Abstract

The attention mechanism can significantly improve the performance of image super-resolution (SR) networks. In this paper, we propose a novel attention-enhanced network (AENet) to explore the long-range modeling ability. Specifically, we propose a Dual-attention Block (DAB), which consists of Large Kernel Convolution Decomposition Attention Block (LKDAB) and Channel Enhanced Structure Unit (CESU), to capture global and local information in spatial and channel dimension. For the purpose to extract multi-scale information at different granularity levels, we design Multi-scale Feature Adaptive Aggregation Block (MFAAB) to aggregate information of different scales. Finally, in order to summarize more reasonable information, we introduce a Reconstruction Sub-block (RSB) in the tail of the network. Extensive experiments on four benchmark datasets demonstrate that our AENet outperforms compared with other state-of-the-art SR models.