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

Armin Mehri, Parichehr B. Ardakani, Angel D. Sappa; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2021, pp. 2704-2713

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.

Related Material


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[bibtex]
@InProceedings{Mehri_2021_WACV, author = {Mehri, Armin and Ardakani, Parichehr B. and Sappa, Angel D.}, title = {MPRNet: Multi-Path Residual Network for Lightweight Image Super Resolution}, booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)}, month = {January}, year = {2021}, pages = {2704-2713} }