MPRNet: Multi-Path Residual Network for Lightweight Image Super Resolution

• URL: http://arxiv.org/abs/2011.04566v1
• Date: Mon, 9 Nov 2020 17:11:15 GMT
• Title: MPRNet: Multi-Path Residual Network for Lightweight Image Super Resolution
• Authors: Armin Mehri, Parichehr B.Ardakani, Angel D.Sappa
• Abstract summary: A novel lightweight super resolution network is proposed, which improves the SOTA performance in lightweight SR. The proposed architecture also contains a new attention mechanism, Two-Fold Attention Module, to maximize the representation ability of the model.
• Score: 2.3576437999036473
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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