@InProceedings{ArminMehri2021,
author="Armin Mehri
and Parichehr Behjati Ardakani
and Angel Sappa",
title="MPRNet: Multi-Path Residual Network for Lightweight Image Super Resolution",
booktitle="IEEE Winter Conference on Applications of Computer Vision",
year="2021",
pages="2703--2712",
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.",
optnote="MSIAU; 600.130; 600.122",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=3582), last updated on Mon, 24 Oct 2022 13:37:14 +0200",
doi="10.1109/WACV48630.2021.00275",
opturl="https://ieeexplore.ieee.org/document/9423169",
file=":http://refbase.cvc.uab.es/files/MAS2021b.pdf:PDF"
}