Papers › MPRNet: Multi-Path Residual Network for Lightweight Image Super Resolution
MPRNet: Multi-Path Residual Network for Lightweight Image Super Resolution
Armin Mehri, Parichehr B. Ardakani, Angel D. Sappa
Lightweight super resolution networks have extremely importance for real-world applications. In recent years several SR deep learning approaches with outstanding achievement have been introduced by sacrificing memory and computational cost. To overcome this problem, a novel lightweight super resolution network is proposed, which improves the SOTA performance in lightweight SR and performs roughly similar to computationally expensive networks. Multi-Path Residual Network designs with a set of Residual concatenation Blocks stacked with Adaptive Residual Blocks: (i) to adaptively extract informative features and learn more expressive spatial context information; (ii) to better leverage multi-level representations before up-sampling stage; and (iii) to allow an efficient information and gradient flow within the network. The proposed architecture also contains a new attention mechanism, Two-Fold Attention Module, to maximize the representation ability of the model. Extensive experiments show the superiority of our model against other SOTA SR approaches.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Video deraining | VRDS | MPRNet | PSNR | 29.53 | #4 of 8 | Archive leaderboard | report |
| Video deraining | VRDS | MPRNet | SSIM | 0.9175 | #4 of 8 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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