ReDU-IR: Reconstructible Feature Decomposition and State Complementary Updating for All-in-One Blind Image Restoration

All-in-One Image Restoration (AiOIR) aims to recover high-quality images from inputs corrupted by heterogeneous degradations using a single model. In hierarchical restoration networks, downsampling may discard information needed for reconstruction. Standard skip connections typically pass encoder information directly to the corresponding decoder stage without updating it based on the restored main state. To address these issues, we propose ReDU-IR. It combines Reconstructible Feature Decomposition (RFD) with State Complementary Updating (SCU). RFD decomposes encoder features into main and complementary representations. The matched inverse transform reconstructs features at the corresponding decoder stage. This design avoids irreversible information loss during scale transitions. SCU applies a shared mapping to the encoder and decoder main states. It uses the difference between the two mapping outputs to update the complementary representation without explicit degradation identification. Extensive experiments on multiple datasets demonstrate the effectiveness of our proposed method and its individual components.