  Super-resolution (SR) has traditionally been based on pairs of
high-resolution images (HR) and their low-resolution (LR) counterparts obtained
artificially with bicubic downsampling. However, in real-world SR, there is a
large variety of realistic image degradations and analytically modeling these
realistic degradations can prove quite difficult. In this work, we propose to
handle real-world SR by splitting this ill-posed problem into two comparatively
more well-posed steps. First, we train a network to transform real LR images to
the space of bicubically downsampled images in a supervised manner, by using
both real LR/HR pairs and synthetic pairs. Second, we take a generic SR network
trained on bicubically downsampled images to super-resolve the transformed LR
image. The first step of the pipeline addresses the problem by registering the
large variety of degraded images to a common, well understood space of images.
The second step then leverages the already impressive performance of SR on
bicubically downsampled images, sidestepping the issues of end-to-end training
on datasets with many different image degradations. We demonstrate the
effectiveness of our proposed method by comparing it to recent methods in
real-world SR and show that our proposed approach outperforms the
state-of-the-art works in terms of both qualitative and quantitative results,
as well as results of an extensive user study conducted on several real image
datasets.
