Lightweight image super-resolution with group-convolutional feature enhanced distillation network

Recently, the application of convolution neural network (CNN) in single image super-resolution (SISR) is gradually developing. Although many CNN-based methods have acquired splendid performance, oversized model complexity hinders their application in real life. In response to this problem, lightweight and efficient are becoming development tendency of SR models. The residual feature distillation network (RFDN) is one of the state-of-the-art lightweight SR networks. However, the shallow residual block (SRB) in RFDN still uses ordinary convolution to extract feature, where still has great improvement room for the reduction of network parameters. In this paper, we propose the Group-convolutional Feature Enhanced Distillation Network (GFEDNet), which is constructed by the stacking of feature distillation and aggregation block (FDAB). Benefitting from residual learning of residual feature aggregation (RFA) framework and feature distillation strategy of RFDN, the FDAB can obtain more diverse and detailed feature representations, thereby improves the SR capability. Furthermore, we propose the multi-scale group convolution block (MGCB) to replace the SRB. Thanks to group convolution and multi-branch parallel structure, the MGCB reduces the parameters substantially while maintaining SR performance. Extensive experiments show the powerful function of our proposed GFEDNet against other state-of-the-art methods.