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.

提出了一种名为 FAN 的频率聚合网络方法，它能够针对复杂的现实降解恢复真实世界低分辨率图像的高分辨率版本，通过提取不同频率的低分辨率图像，并分别传递到一种称为 CA-GRDB 的通道注意力聚合残差密集网络中，最后自适应地聚合这些残余特征图来恢复具有增强细节和纹理的高分辨率图像，实验证明该方法在 AIM2020 挑战赛的真实图像超分辨率任务上表现出色，最终权衡指标 PSNR 达到 31.1735，SSIM 达到 0.8728，我们的团队 SR-IM 获得该赛道的第四名。