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.

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