%0 Conference Proceedings
%T MPRNet: Multi-Path Residual Network for Lightweight Image Super Resolution
%A Armin Mehri
%A Parichehr Behjati Ardakani
%A Angel Sappa
%B IEEE Winter Conference on Applications of Computer Vision
%D 2021
%F Armin Mehri2021
%O MSIAU; 600.130; 600.122
%O exported from refbase (http://refbase.cvc.uab.es/show.php?record=3582), last updated on Mon, 24 Oct 2022 13:37:14 +0200
%X 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.
%U https://ieeexplore.ieee.org/document/9423169
%U http://refbase.cvc.uab.es/files/MAS2021b.pdf
%U http://dx.doi.org/10.1109/WACV48630.2021.00275
%P 2703-2712