Year: 2025
Type: article
Abstract: Single Image Super-Resolution (SISR) aims to reconstruct high-resolution (HR) images from low-resolution (LR) inputs, serving as a core task in computer vision. Despite recent advances, existing methods often struggle to balance structural fidelity and computational efficiency. To address this, we propose PFRNet, a lightweight superpixel-aware model integrating superpixel segmentation, local attention aggregation, and global structure modeling. The framework comprises four key modules: GASS, SPDF, SPFA, and LAP, jointly enabling multi-scale and structure-consistent feature learning. Experiments on benchmark datasets (e.g., Set5, Urban100) show that PFRNet achieves superior performance with fewer parameters. Ablation studies further verify the effectiveness of each module.
Authors: Rui Xu, Wei Fan
Institution: Chongqing University of Technology
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4411792920