  Single image super-resolution (SISR) aims to recover the high-resolution (HR)
image from its low-resolution (LR) input image. With the development of deep
learning, SISR has achieved great progress. However, It is still a challenge to
restore the real-world LR image with complicated authentic degradations.
Therefore, we propose FAN, a frequency aggregation network, to address the
real-world image super-resolu-tion problem. Specifically, we extract different
frequencies of the LR image and pass them to a channel attention-grouped
residual dense network (CA-GRDB) individually to output corresponding feature
maps. And then aggregating these residual dense feature maps adaptively to
recover the HR image with enhanced details and textures. We conduct extensive
experiments quantitatively and qualitatively to verify that our FAN performs
well on the real image super-resolution task of AIM 2020 challenge. According
to the released final results, our team SR-IM achieves the fourth place on the
X4 track with PSNR of 31.1735 and SSIM of 0.8728.
