Flow-based methods have demonstrated promising results in addressing the ill-posed nature of super-resolution (SR) by learning the distribution of high-resolution (HR) images with the normalizing flow. However, these methods can only perform a predefined fixed-scale SR, limiting their potential in real-world applications. Meanwhile, arbitrary-scale SR has gained more attention and achieved great progress. Nonetheless, previous arbitrary-scale SR methods ignore the ill-posed problem and train the model with per-pixel L1 loss, leading to blurry SR outputs. In this work, we propose "Local Implicit Normalizing Flow" (LINF) as a unified solution to the above problems. LINF models the distribution of texture details under different scaling factors with normalizing flow. Thus, LINF can generate photo-realistic HR images with rich texture details in arbitrary scale factors. We evaluate LINF with extensive experiments and show that LINF achieves the state-of-the-art perceptual quality compared with prior arbitrary-scale SR methods.

翻译：基于流的方法通过学习高分辨率图像在归一化流中的分布，已在解决超分辨率的不适定问题上展现出令人瞩目的成果。然而，这些方法仅能执行预定义的固定尺度超分辨率，限制了其在真实场景中的应用潜力。与此同时，任意尺度超分辨率受到更多关注并取得了重大进展。尽管如此，先前的任意尺度超分辨率方法忽略了不适定问题，采用逐像素L1损失训练模型，导致生成模糊的超分辨率输出。本文提出“局部隐式归一化流”（LINF）作为上述问题的统一解决方案。LINF利用归一化流对不同缩放因子下的纹理细节分布进行建模，从而能够在任意缩放因子下生成具有丰富纹理细节的照片级真实感高分辨率图像。通过大量实验评估，我们证明LINF与先前的任意尺度超分辨率方法相比，达到了最先进的感知质量。