Interactive multi-feature residual network for lightweight image super-resolution
Jiaqi Tang, Sichen Guo, Heyou Chang, Guangwei Gao · Cognitive Robotics · 2026
Image Super-Resolution (SR) aims to recover high-resolution (HR) images from their low-resolution (LR) counterparts. However, existing SR methods suffer from insufficient multi-level feature interaction, leading to increased computational complexity. To address this limitation, we propose an Interactive Multi-Feature Residual Network (IMFRN) for lightweight SR. To facilitate feature exchange across different levels, we propose the Interactive Distillation Feature Refinement Module (IDFRM), which refines hierarchical features through cross-stage distillation and residual aggregation. IDFRM includes the Multi-Branch Feature Attention Block (MFAB) to integrate spatial and channel information from multiple branches adaptively. Additionally, the Dual Attention Fusion Module (DAFM) dynamically enhances feature representations using complementary attention mechanisms. To strengthen the global context, we integrate a Transformer-based module. Our IMFRN effectively facilitates interaction between features at different levels, achieving state-of-the-art performance with reduced parameters and computational cost.