MPRNet: Multi-Path Residual Network for Lightweight Image Super Resolution
This addresses the need for efficient image super-resolution in real-world applications, representing an incremental improvement in lightweight network design.
The paper tackles the problem of high computational cost in super-resolution networks by proposing MPRNet, a lightweight architecture that achieves state-of-the-art performance in lightweight super-resolution and performs similarly to more expensive networks.
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.