Multi-scale Attention Network for Single Image Super-Resolution

arXiv:2209.14145v3 Announce Type: replace-cross
Abstract: ConvNets can compete with transformers in high-level tasks by exploiting larger receptive fields. To unleash the potential of ConvNet in super-resolution, we propose a multi-scale attention network (MAN), by coupling classical multi-scale mechanism with emerging large kernel attention. In particular, we proposed multi-scale large kernel attention (MLKA) and gated spatial attention unit (GSAU). Through our MLKA, we modify large kernel attention with multi-scale and gate schemes to obtain the abundant attention map at various granularity …

abstract arxiv attention cs.cv eess.iv fields image kernel network resolution scale tasks transformers type