Single image super-resolution (SISR) is a challenging ill-posed problem that aims to up-sample a given low-resolution (LR) image to a high-resolution (HR) counterpart. Due to the difficulty in obtaining real LR-HR training pairs, recent approaches are trained on simulated LR images degraded by simplified down-sampling operators, e.g., bicubic. Such an approach can be problematic in practice because of the large gap between the synthesized and real-world LR images. To alleviate the issue, we propose a novel Invertible scale-Conditional Function (ICF), which can scale an input image and then restore the original input with different scale conditions. By leveraging the proposed ICF, we construct a novel self-supervised SISR framework (ICF-SRSR) to handle the real-world SR task without using any paired/unpaired training data. Furthermore, our ICF-SRSR can generate realistic and feasible LR-HR pairs, which can make existing supervised SISR networks more robust. Extensive experiments demonstrate the effectiveness of the proposed method in handling SISR in a fully self-supervised manner. Our ICF-SRSR demonstrates superior performance compared to the existing methods trained on synthetic paired images in real-world scenarios and exhibits comparable performance compared to state-of-the-art supervised/unsupervised methods on public benchmark datasets.

翻译：单图像超分辨率（SISR）是一个具有挑战性的不适定问题，旨在将给定的低分辨率（LR）图像上采样为高分辨率（HR）图像。由于难以获取真实的LR-HR训练对，现有方法通常使用经简化下采样算子（如双三次插值）退化的模拟LR图像进行训练。由于合成LR图像与真实世界LR图像之间存在巨大差距，此类方法在实践中可能存在问题。为解决这一问题，我们提出了一种新颖的可逆尺度条件函数（ICF），它能对输入图像进行缩放，然后通过不同尺度条件恢复原始输入。通过利用所提出的ICF，我们构建了一个新颖的自监督SISR框架（ICF-SRSR），用于处理无需任何配对/非配对训练数据的真实世界SR任务。此外，我们的ICF-SRSR能生成真实且可行的LR-HR对，从而增强现有监督式SISR网络的鲁棒性。大量实验证明了该方法在完全自监督方式下处理SISR的有效性。在真实场景中，我们的ICF-SRSR相比现有基于合成配对图像训练的方法展现出更优性能，并在公开基准数据集上与最先进的监督式/非监督式方法性能相当。