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.

通过借助提出的可逆尺度条件函数（ICF），我们构建了一种新颖的自监督单图超分辨率（SISR）框架（ICF-SRSR），用于处理真实世界中的 SR 任务，而无需使用任何配对 / 非配对训练数据，并且我们的 ICF-SRSR 能够生成逼真且可行的低分辨率 - 高分辨率图像对，使得现有的监督 SISR 网络更加鲁棒。广泛的实验证明了我们所提出方法在处理 SISR 中以完全自监督的方式的有效性。与在真实世界情境中使用合成配对图像进行训练的现有方法相比，我们的 ICF-SRSR 表现出了更优越的性能，并且在公共基准数据集上与最先进的监督 / 无监督方法相当。