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

TL;DR

MPRNet is a lightweight image super-resolution network that combines multi-path residual design and a novel attention mechanism to achieve state-of-the-art performance with low computational cost.

Contribution

The paper introduces a new lightweight super-resolution network with multi-path residual architecture and a Two-Fold Attention Module, improving performance while maintaining efficiency.

Findings

Outperforms existing lightweight SR models in accuracy

Achieves comparable results to more complex networks

Demonstrates efficiency in real-world applications

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.