Lightweight Single Image Super-Resolution through Efficient Second-order Attention Spindle Network

Recent years have witnessed great success of applying deep convolutional neural networks (CNNs) to single image super-resolution (SISR). However, most of these algorithms focus on increasing modeling capability through developing deeper and wider networks, improving the performance but at a cost of huge computation. Targeting at a better trade-off between efficiency and effectiveness, we propose ESASN, an efficient second-order attention spindle network for lightweight SISR. ESASN is built upon efficient second-order attention spindle (ESAS) blocks, each of which contains two well-designed new modules, efficient multi-scale (EMS) module and second-order attention (SOA) module. EMS reduces a considerable number of parameters while retaining the multi-scale structure to explore rich features. SOA further rescales the multi-scale feature maps, capturing the inter-dependencies among channels pixel-wisely with little additional cost. Both qualitative and quantitative experimental results demonstrate that the combination of EMS and SOA works out favorably for SISR, lifting the performance with fewer parameters. Code is available at https://github.com/yiyunchen/ESASN.