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.

本论文通过采用分层 S 在变压器网络以及预训练超分辨率任务获得的特征表示，提出了一种新的压缩图像超分辨率（Compressed Image Super-Resolution，CISR）模型HST，并且通过与不同的预训练任务（如双三次插值和真实超分辨率模拟）合作，并在AIM2022挑战赛中取得了排名第五的优异结果，具有23.51dB的PSNR，在所进行的大规模实验和剪枝研究中也验证了HST模型的有效性。

HST: 基于层次化 Swin Transformer 的压缩图像超分辨率方法