论文标题
风扇：真实图像超分辨率的频率聚合网络
FAN: Frequency Aggregation Network for Real Image Super-resolution
论文作者
论文摘要
单图像超分辨率（SISR）旨在从其低分辨率（LR）输入图像中恢复高分辨率（HR）图像。随着深度学习的发展，SISR取得了巨大的进步。但是，以复杂的真实降解恢复现实世界的LR图像仍然是一个挑战。因此，我们提出了一个频率聚合网络的风扇，以解决现实世界图像超级蛋白质问题。具体而言，我们提取LR图像的不同频率，并将它们分别传递到注意力组的残留密度网络（CA-GRDB）中，以输出相应的特征图。然后将这些残留的密集特征汇总为自适应地恢复HR图像，并具有增强的细节和纹理。我们在定量和质量上进行广泛的实验，以验证我们的粉丝在AIM 2020挑战的真实图像超分辨率任务上表现良好。根据发布的最终结果，我们的SR-IM团队以31.1735的PSNR为X4赛道上的第四名，SSIM为0.8728。
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.
