Most image super-resolution (SR) methods are developed on synthetic low-resolution (LR) and high-resolution (HR) image pairs that are constructed by a predetermined operation, e.g., bicubic downsampling. As existing methods typically learn an inverse mapping of the specific function, they produce blurry results when applied to real-world images whose exact formulation is different and unknown. Therefore, several methods attempt to synthesize much more diverse LR samples or learn a realistic downsampling model. However, due to restrictive assumptions on the downsampling process, they are still biased and less generalizable. This study proposes a novel method to simulate an unknown downsampling process without imposing restrictive prior knowledge. We propose a generalizable low-frequency loss (LFL) in the adversarial training framework to imitate the distribution of target LR images without using any paired examples. Furthermore, we design an adaptive data loss (ADL) for the downsampler, which can be adaptively learned and updated from the data during the training loops. Extensive experiments validate that our downsampling model can facilitate existing SR methods to perform more accurate reconstructions on various synthetic and real-world examples than the conventional approaches.
翻译:多数图像超分辨率(SR)方法都是在合成低分辨率(LR)和高分辨率(HR)图像配对上开发的,这些配对是通过预先设定的操作(例如双立下取样)构建的,例如双立下取样。由于现有方法通常会对特定功能进行反向映射,因此在应用到真实形状不同和未知的真实世界图像时会产生模糊的结果。因此,有几种方法试图合成更多样化的LR样本或学习一个现实的下游取样模型。然而,由于对下游取样过程的限制性假设,它们仍然有偏向性,而且不太普遍。本研究提出了一种新颖的方法来模拟未知的下游采样过程,而不强加限制性的先前知识。我们提议在对抗性培训框架中采用一种可普遍实现的低频损失(LFL)方法,以模拟目标LR图像的分布,而不使用任何配对的例子。此外,我们为下游取样员设计了适应性数据损失(ADL),这可以在培训过程中从数据中适应性地学习和更新。广泛的实验证实,我们的下游模型可以促进现有的SR方法,而不是在各种合成世界上进行更精确的常规的重建。