s-LMPNet: a super-lightweight multi-stage progressive network for image super-resolution

Single image super-resolution (SISR) has achieved great success in recent years due to the representation ability of large and deep models. However, these models usually have a large number of network parameters, which hinders their application to real-world scenarios. To reduce the number of parameters in the SISR models, we propose a super-lightweight model termed s-LMPNet with a multi-stage architecture. Specifically, s-LMPNet includes three sub-networks, which are organized in a cascaded way. Each sub-network is constructed with multiple lightweight cross-group skip-connecting blocks (CGSCBs). To enhance the model performance, a residual feature fusion attention module is adopted to integrate intermediate features from different CGSCBs in a self-adaptive weighted way. A cross-stage feature propagation module is used to propagate the information from the low stage to the high stage, thereby making the network optimization procedure more stable. Extensive experiments are performed on commonly-used super-resolution benchmarks. Experiment results have shown that s-LMPNet achieves promising performance compared to other state-of-the-art lightweight super-resolution methods.