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
MLKA, we rectify LKA with multi-scale and gate schemes to obtain the abundant
attention map at various granularity levels, therefore jointly aggregating
global and local information and avoiding the potential blocking artifacts. In
GSAU, we integrate gate mechanism and spatial attention to remove the
unnecessary linear layer and aggregate informative spatial context. To confirm
the effectiveness of our designs, we evaluate MAN with multiple complexities by
simply stacking different numbers of MLKA and GSAU. Experimental results
illustrate that our MAN can achieve varied trade-offs between state-of-the-art
performance and computations. Code is available at
this https URL

本文提出了一个卷积神经网络（CNN）的多尺度关注网络（MAN），其中包括多尺度大型核关注（MLKA）和门控空间关注单元（GSAU），以提高卷积 SR 网络的性能。实验结果表明，我们的 MAN 可以在不同的状态下实现各种平衡。