  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.
