Lightweight Image Super-resolution Model Fusing Detail Enhancement with Dynamic Up-sampling

YU Mingwei

doi:10.19554/j.cnki.1001-3563.2026.09.030

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Packaging Engineering ›› 2026, Vol. 47 ›› Issue (9) : 286-295. DOI: 10.19554/j.cnki.1001-3563.2026.09.030

Automatic and Intelligent Technology

Lightweight Image Super-resolution Model Fusing Detail Enhancement with Dynamic Up-sampling

YU Mingwei^*

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Abstract

To address high complexity and computational cost in image super-resolution models, the work aims to improve reconstruction accuracy and efficiency. The Enhanced Dual Aggregation Transformer (EDAT) was proposed based on the Dual Aggregation Transformer (DAT), fusing image detail enhancement with dynamic up-sampling. By introducing an edge attention and image sharpening enhancement module, the representation capability of local edges and global details was improved. Dynamic up-sampling was adopted to replace traditional up-sampling methods, which significantly reduced model complexity and parameter count while maintaining reconstruction performance. On 4-fold super-resolution tasks, EDAT outperformed DAT, CAT, Swin2SR, SwinIR, and SRCNN on Set5 and Set14. Ablation studies showed EDAT reduced model complexity by approximately 65%, while still improving reconstruction RMSE by about 1.5% compared to DAT. Without extra input information, EDAT effectively balances reconstruction accuracy and efficiency, demonstrating strong potential in resource-constrained and real-time applications.

Key words

image super-resolution / edge attention / image sharpening / dynamic up-sampling / lightweight model

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YU Mingwei. Lightweight Image Super-resolution Model Fusing Detail Enhancement with Dynamic Up-sampling[J]. Packaging Engineering. 2026, 47(9): 286-295 https://doi.org/10.19554/j.cnki.1001-3563.2026.09.030

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