SPIFNet: Structure Preservation and Interactive Fusion Network for Image Super-Resolution Restoration Algorithm

Traditional image super-resolution and deblurring methods typically handle resolution enhancement and blur removal separately, and often assume a single type of degradation. However, in real-world scenarios, images frequently suffer from multiple simultaneous degradations, such as motion blur and resolution loss, which these methods struggle to address effectively. Specifically, classical super-resolution methods tend to amplify blur artifacts when increasing resolution, while deblurring methods may lose structural details during restoration. To overcome these limitations, this paper proposes a novel Structure Preservation and Interactive Fusion Network (SPIFNet) that integrates semantic priors from deep learning with edge-aware structural guidance. First, based on the prior knowledge that edge information is crucial for image super-resolution and deblurring tasks, a structure-preserving module based on edge maps is proposed. This module utilizes edge-aided information and multi-scale edge constraints to enhance the structural and detail preservation capabilities of the reconstruction network. Additionally, to obtain accurate edge information of blurry images for building the structure-preserving module, a multi-branch fusion-structured edge extraction network is introduced. Furthermore, to fully utilize the relationship between image deblurring and super-resolution tasks, an edge-guided interactive fusion module is proposed to adaptively fuse feature information from deblurring and super-resolution tasks. To improve the network’s capacity to represent features, local and global attention methods are used. The suggested approach for super-resolution of fuzzy images has been rigorously validated by both quantitative and qualitative testing findings.