  This work aims at designing a lightweight convolutional neural network for
image super resolution (SR). With simplicity bare in mind, we construct a
pretty concise and effective network with a newly proposed pixel attention
scheme. Pixel attention (PA) is similar as channel attention and spatial
attention in formulation. The difference is that PA produces 3D attention maps
instead of a 1D attention vector or a 2D map. This attention scheme introduces
fewer additional parameters but generates better SR results. On the basis of
PA, we propose two building blocks for the main branch and the reconstruction
branch, respectively. The first one - SC-PA block has the same structure as the
Self-Calibrated convolution but with our PA layer. This block is much more
efficient than conventional residual/dense blocks, for its twobranch
architecture and attention scheme. While the second one - UPA block combines
the nearest-neighbor upsampling, convolution and PA layers. It improves the
final reconstruction quality with little parameter cost. Our final model- PAN
could achieve similar performance as the lightweight networks - SRResNet and
CARN, but with only 272K parameters (17.92% of SRResNet and 17.09% of CARN).
The effectiveness of each proposed component is also validated by ablation
study. The code is available at https://github.com/zhaohengyuan1/PAN.
