Although recent diffusion-based single-step super-resolution methods achieve better performance as compared to SinSR, they are computationally complex. To improve the performance of SinSR, we investigate preserving the high-frequency detail features during super-resolution (SR) because the downgraded images lack detailed information. For this purpose, we introduce a high-frequency perceptual loss by utilizing an invertible neural network (INN) pretrained on the ImageNet dataset. Different feature maps of pretrained INN produce different high-frequency aspects of an image. During the training phase, we impose to preserve the high-frequency features of super-resolved and ground truth (GT) images that improve the SR image quality during inference. Furthermore, we also utilize the Jenson-Shannon divergence between GT and SR images in the pretrained DINO-v2 embedding space to match their distribution. By introducing the $\textbf{h}igh$- $\textbf{f}requency$ preserving loss and distribution matching constraint in the single-step $\textbf{diff}usion-based$ SR ($\textbf{HF-Diff}$), we achieve a state-of-the-art CLIPIQA score in the benchmark RealSR, RealSet65, DIV2K-Val, and ImageNet datasets. Furthermore, the experimental results in several datasets demonstrate that our high-frequency perceptual loss yields better SR image quality than LPIPS and VGG-based perceptual losses. Our code will be released at https://github.com/shoaib-sami/HF-Diff.

本研究针对传统单步超分辨率方法在计算复杂性方面的不足，通过保留高频细节特征来提升超分辨率图像的质量。我们引入了一种高频感知损失，并结合Jenson-Shannon散度，以更好地匹配超分辨率图像与真实图像的分布。实验表明，该方法在多个基准数据集上取得了最先进的表现，并优于现有的感知损失方法。

HF-Diff：高频感知损失和分布匹配用于一步法基于扩散的图像超分辨率