  Convolutional neural networks based single-image super-resolution (SISR) has
made great progress in recent years. However, it is difficult to apply these
methods to real-world scenarios due to the computational and memory cost.
Meanwhile, how to take full advantage of the intermediate features under the
constraints of limited parameters and calculations is also a huge challenge. To
alleviate these issues, we propose a lightweight yet efficient Feature
Distillation Interaction Weighted Network (FDIWN). Specifically, FDIWN utilizes
a series of specially designed Feature Shuffle Weighted Groups (FSWG) as the
backbone, and several novel mutual Wide-residual Distillation Interaction
Blocks (WDIB) form an FSWG. In addition, Wide Identical Residual Weighting
(WIRW) units and Wide Convolutional Residual Weighting (WCRW) units are
introduced into WDIB for better feature distillation. Moreover, a Wide-Residual
Distillation Connection (WRDC) framework and a Self-Calibration Fusion (SCF)
unit are proposed to interact features with different scales more flexibly and
efficiently.Extensive experiments show that our FDIWN is superior to other
models to strike a good balance between model performance and efficiency. The
code is available at https://github.com/IVIPLab/FDIWN.
