Lightweight blueprint residual network for single image super-resolution

Abstract

The application of deep convolutional neural networks (CNNs) makes the lightweight single image super-resolution (SISR) task develop rapidly in recent years. However, existing lightweight SISR networks for the terminals with limited computing resources are still not efficient enough. In order to further alleviate the model complexity while keeping remarkable reconstruction performance, in this paper, we propose a lightweight blueprint residual network (LBRN), which is a powerful CNN-based SR model while simultaneously maintaining extremely lightweight. Specifically, we design a novel lightweight blueprint residual block (LBRB) to learn the high-frequency information efficiently for lightweight SISR. The LBRB with fewer parameters consists of more efficient convolution operations to extract high-frequency information which helps recover more visual details. In addition, we propose an effective feature fusion residual block (EFFRB) which is composed of three LBRBs with convolutional kernels of different sizes. The EFFRB, which is designed with effective multi-scale receptive fields, can availably extract and fuse multi-scale high-frequency information to obtain discriminative feature, and it further improves model performance. Extensive experiments show that our LBRN achieves superior reconstruction performance quantitatively and visually compared with other lightweight state-of-the-art SR methods.