Lightweight Image Super-Resolution Via Superpixel Prior and Feature Reinforcement
DOI:
https://doi.org/10.53469/jrse.2025.07(06).03Keywords:
Image Super-Resolution, Superpixel Awareness, Lightweight, Structural ModelingAbstract
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.
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Copyright (c) 2025 Rui Xu, Wei Fan

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.