  The attention mechanism plays a pivotal role in designing advanced
super-resolution (SR) networks. In this work, we design an efficient SR network
by improving the attention mechanism. We start from a simple pixel attention
module and gradually modify it to achieve better super-resolution performance
with reduced parameters. The specific approaches include: (1) increasing the
receptive field of the attention branch, (2) replacing large dense convolution
kernels with depth-wise separable convolutions, and (3) introducing pixel
normalization. These approaches paint a clear evolutionary roadmap for the
design of attention mechanisms. Based on these observations, we propose VapSR,
the VAst-receptive-field Pixel attention network. Experiments demonstrate the
superior performance of VapSR. VapSR outperforms the present lightweight
networks with even fewer parameters. And the light version of VapSR can use
only 21.68% and 28.18% parameters of IMDB and RFDN to achieve similar
performances to those networks. The code and models are available at
https://github.com/zhoumumu/VapSR.
