Multi-feature fusion attention network for single image super-resolution

Single Image Super-Resolution algorithms have made enormous progress in recent years. However, many previous Convolution Neural Network (CNN) based Super-Resolution algorithms only stack uniform convolution layers of fixed kernel size, and frequently ignore inherent multi-scale properties of the images, resulting in unsatisfactory reconstruction results. Here, a multi-feature fusion attention network (MFFAN) is proposed for capturing information at diverse scales. MFFAN is composed of multiple efficient sparse residual group (ESRG) modules. Several multi-scale feature fusion blocks (MSFFB) are constructed using a cascade manner in each ESRG module and it is capable of exploiting various cross scales information. Subsequently, a local-global spatial attention block (LGSAB) is inserted at the tail of the ESRG module for further improving the interaction of inter-pixel, which strengths essential features and suppresses irrelevant information. Additionally, owing to the fact that only feeding final output into the reconstruction layer has exacerbated the long-range dependency problems, an enhanced hierarchy feature fusion block (EHFFB) is designed to fuse low-level information and high-level semantic information. Experiment results indicate that the proposed MFFAN is competitive in comparison to several state-of-the-art algorithms.