Feature Distillation Interaction Weighting Network for Lightweight Image Super-resolution

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