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.
翻译:图像压缩超分辨率近年来引起了极大关注,图像随着压缩工艺品和低分辨率文物而退化。由于复杂的混合扭曲现象,很难通过超分辨率和压缩工艺品清除的简单合作来恢复扭曲的图像。在本文中,我们向前迈出了一步,建议使用高分辨率Swin变异器(HST)网络来恢复低分辨率压缩图像,以共同捕捉等级特征表现,并分别加强Swin变异器的每个比例。此外,我们发现超分辨率(SR)任务前的训练在压缩图像超分辨率中至关重要。为了探索不同SR前训练的效果,我们把常用的SR任务(例如双立方和不同的真实超分辨率模拟)作为我们的培训前任务,并表明斯洛伐克共和国在压缩图像超分辨率中发挥着不可替代的作用。在高分辨率和预培训合作下,我们的HST在低质量压缩图像超分辨率轨道上取得了第五位挑战。我们提议的PSNR的实验和23.51B范围研究的有效性。