SREFBN: Enhanced feature block network for single‐image super‐resolution

Deep learning has assisted the field of single-image super-resolution (SR) in achieving new heights. However, the task of restoring a high-resolution (HR) image from a highly degraded low-resolution (LR) image is sophisticated due to poor image restoration quality. A novel and effective lightweight SR method is presented as super-resolution via an enhanced feature block network (SREFBN) that successfully reconstructs an HR image using a corresponding LR image with a purposed deep residual block. In addition, a novel shared parameters approach in the top-down pathway among low-level feature maps is introduced. The experimental results prove that SREFBN achieves remarkable performance. The presented framework requires lower computational cost and outperforms many state-of-the-art methods. It is also highly adaptable with low-end devices, requiring lower multiplication and adding operations. A trade-off comparison between the number of parameters, execution time, and accuracies is given while also showing different variations of our approach to prove the effectiveness and reliability of the shared parameters. Most importantly, the results indicate that our framework has gained state-of-the-art performance on larger scales 3 and 4. Code is available at .