Lightweight image super-resolution via an efficient local–global transformer network with adaptive attention windows
摘要
Recently, there has been a growing interest in developing lightweight models for single-image super-resolution (SISR) in the graphics community. However, a common limitation of most existing methods is they have a restricted receptive field and struggle to utilize local–global feature information for restoration. To address this issue, we propose a lightweight SR network named LLGTN, in which a local transformer module (LTM) and a global transformer module (GTM) with adaptive attention windows are developed for efficiently extracting local–global feature information. Specifically, LTM calculates self-attention (SA) on multiple small window sizes to extract multi-scale features, which enhances the model’s capability to capture contextual information. In contrast, GTM calculates SA on a large window size by utilizing linear attention to efficiently extract global features. Extensive experimental evaluations demonstrate that LLGTN achieves state-of-the-art performance across five representative natural images SR datasets with fewer parameters and lower computational costs compared to existing lightweight SISR methods (e.g., the PSNR on the Urban100 dataset with a scaling factor of