Multi-branch-feature fusion super-resolution network

Abstract

Deep network attracts extensive attention in the single image super-resolution field. Most of the deep network-based super-resolution methods usually employ a feature extraction stream to generate the input for the next extraction module during extracting deep features. Whereas, many experiments show that the feature maps yielded by different feature extraction streams generally contain different but complementary detailed information. Based on this observation, we propose a multi-branch feature fusion super-resolution network termed as MBFSR to solve the single image super-resolution task. MBFSR progressively deploys multiple feature extraction modules to extract the deep feature. Specifically, the deployed feature extraction modules first generate different feature maps through three well-designed feature extraction branches, and then fuse them by the developed three-branch fusion module to generate a high-resolution feature map. Finally, the output features of all modules are directly used to generate the desired high-resolution image. Experimental results demonstrate the availability and superiority of MBFSR over other popular methods.