HGLN: Hybrid Gated Large-Kernel Network for Lightweight Image Super-Resolution
Year: 2026
Type: article
Abstract: Recent large-kernel based SISR methods often struggle to balance global structural consistency with local texture preservation while maintaining computational efficiency. To address this, we propose the Hybrid Gated Large-kernel Network (HGLN). First, the Hybrid Multi-Scale Aggregation (HMSA) decouples features into structural and detailed streams via dual-path processing, utilizing a modified Large Kernel Attention to capture long-range interactions. Second, the Local–Global Synergistic Attention (LGSA) recalibrates features by integrating local spatial context with dual global statistics (mean and standard deviation). Finally, the Structure-Gated Feed-forward Network (SGFN) leverages high-frequency residuals to modulate the gating mechanism for precise edge restoration. Extensive experiments demonstrate that HGLN outperforms state-of-the-art methods. Notably, on the challenging Urban100 dataset (×4), HGLN achieves significant PSNR gains with extremely low complexity (only 11G Multi-Adds), proving its suitability for resource-constrained applications.
Source: Applied Sciences
DOI: 10.3390/app16031382
Authors: Man Zhao, Jinkai Niu, Xiang Li
Institution: China University of Geosciences
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W7126086504
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