Image super-resolution (SR) aims to reconstruct high-resolution (HR) images with more details from low-resolution (LR) images. Recently, deep learning-based image SR methods have made significant progress inreconstruction performance through deeper networkmodels andlarge-scale training datasets, but these improvements place higher demands on both computing power and memory resources, thus requiring more efficient solutions.

Then, we give an illustration of operation details in the GraphAgg. B presents further analysis and discussions onour proposed GraphAgg module and IGNN network. Denote the feature shapes ofEL s and EL as H/s W/s and H W respectively. Each LR patch ofEL find thek nearest neighboring LR patches fromEL s. In this section, we first present more ablation experiments to demonstrate the effectiveness of the proposedIGNNfurther,includingtheeffectofusing F0LandFL sandnumberofGraphAggmodules insertedinnetworks.