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