Effective Utilization of Hybrid Residual Modules in Deep Neural Networks for Super Resolution

摘要

Recently, Single-Image Super-Resolution (SISR) has attracted a lot of researchers due to its numerous real-life applications in multiple domains. This paper focuses on efficient solutions of SISR with Hybrid Residual Modules (HRM). The proposed HRM allows the deep neural network to reconstruct very high quality super-resolved images with much lower computation compared to the conventional SISR methods. In this paper, we first describe the technical details of our HRM in SISR and introduce interesting applications of the proposed SISR method, such as surveillance camera system, medical imaging, astronomical imaging.