  Compressed Image Super-resolution has achieved great attention in recent
years, where images are degraded with compression artifacts and low-resolution
artifacts. Since the complex hybrid distortions, it is hard to restore the
distorted image with the simple cooperation of super-resolution and compression
artifacts removing. In this paper, we take a step forward to propose the
Hierarchical Swin Transformer (HST) network to restore the low-resolution
compressed image, which jointly captures the hierarchical feature
representations and enhances each-scale representation with Swin transformer,
respectively. Moreover, we find that the pretraining with Super-resolution (SR)
task is vital in compressed image super-resolution. To explore the effects of
different SR pretraining, we take the commonly-used SR tasks (e.g., bicubic and
different real super-resolution simulations) as our pretraining tasks, and
reveal that SR plays an irreplaceable role in the compressed image
super-resolution. With the cooperation of HST and pre-training, our HST
achieves the fifth place in AIM 2022 challenge on the low-quality compressed
image super-resolution track, with the PSNR of 23.51dB. Extensive experiments
and ablation studies have validated the effectiveness of our proposed methods.
The code and models are available at
https://github.com/USTC-IMCL/HST-for-Compressed-Image-SR.
