  Most single image super-resolution (SR) methods are developed on synthetic
low-resolution (LR) and high-resolution (HR) image pairs, which are simulated
by a predetermined degradation operation, e.g., bicubic downsampling. However,
these methods only learn the inverse process of the predetermined operation, so
they fail to super resolve the real-world LR images; the true formulation
deviates from the predetermined operation. To address this problem, we propose
a novel supervised method to simulate an unknown degradation process with the
inclusion of the prior hardware knowledge of the imaging system. We design an
adaptive blurring layer (ABL) in the supervised learning framework to estimate
the target LR images. The hyperparameters of the ABL can be adjusted for
different imaging hardware. The experiments on the real-world datasets validate
that our degradation model can estimate LR images more accurately than the
predetermined degradation operation, as well as facilitate existing SR methods
to perform reconstructions on real-world LR images more accurately than the
conventional approaches.
