Multi-scale Attention Network for Single Image Super-Resolution. (arXiv:2209.14145v2 [eess.IV] UPDATED)

By exploiting large kernel decomposition and attention mechanisms,
convolutional neural networks (CNN) can compete with transformer-based methods
in many high-level computer vision tasks. However, due to the advantage of
long-range modeling, the transformers with self-attention still dominate the
low-level vision, including the super-resolution task. In this paper, we
propose a CNN-based multi-scale attention network (MAN), which consists of
multi-scale large kernel attention (MLKA) and a gated spatial attention unit
(GSAU), to improve the performance of convolutional SR networks. Within our …

arxiv attention image network scale