论文标题
MPRNET：轻量级图像超级分辨率的多路剩余网络
MPRNet: Multi-Path Residual Network for Lightweight Image Super Resolution
论文作者
论文摘要
轻型超级分辨率网络对于现实世界应用非常重要。近年来，通过牺牲记忆和计算成本，已经引入了几种具有出色成就的SR深度学习方法。为了克服这个问题，提出了一个新型的轻质超级分辨率网络，该网络改善了轻量级SR中的SOTA性能，并且执行与计算昂贵的网络大致相似。多路剩余网络设计具有一组残留的串联块，上面堆叠着自适应残差块：（$ i $），以适应性地提取信息性功能并学习更多表现力的空间上下文信息； （$ ii $）在上采样阶段之前更好地利用多层表示； （$ iii $）允许网络中有效的信息和梯度流。所提出的体系结构还包含一种新的注意机制，即两倍的注意模块，以最大程度地提高模型的表示能力。广泛的实验表明，我们的模型与其他SOTA SR方法的优越性。
Lightweight super resolution networks have extremely importance for real-world applications. In recent years several SR deep learning approaches with outstanding achievement have been introduced by sacrificing memory and computational cost. To overcome this problem, a novel lightweight super resolution network is proposed, which improves the SOTA performance in lightweight SR and performs roughly similar to computationally expensive networks. Multi-Path Residual Network designs with a set of Residual concatenation Blocks stacked with Adaptive Residual Blocks: ($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.
