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.
If you find MPRNet interesting, please cite
@article{mehri2020mprnet,
title={MPRNet: Multi-Path Residual Network for Lightweight Image Super Resolution},
author={Mehri, Armin and Ardakani, Parichehr B and Sappa, Angel D},
journal={arXiv preprint arXiv:2011.04566},
year={2020}
}