Learning Dual-Level Deformable Implicit Representation for Real-World
  Scale Arbitrary Super-Resolution
Zhiheng Li Muheng Li Jixuan Fan Lei Chen Yansong Tang Jie Zhou Jiwen Lu
Scale arbitrary super-resolution based on implicit image function gains
increasing popularity since it can better represent the visual world in a
continuous manner. However, existing scale arbitrary works are trained and
evaluated on simulated datasets, where low-resolution images are generated from
their ground truths by the simplest bicubic downsampling. These models exhibit
limited generalization to real-world scenarios due to the greater complexity of
real-world degradations. To address this issue, we build a RealArbiSR dataset,
a new real-world super-resolution benchmark with both integer and non-integer
scaling factors for the training and evaluation of real-world scale arbitrary
super-resolution. Moreover, we propose a Dual-level Deformable Implicit
Representation (DDIR) to solve real-world scale arbitrary super-resolution.
Specifically, we design the appearance embedding and deformation field to
handle both image-level and pixel-level deformations caused by real-world
degradations. The appearance embedding models the characteristics of
low-resolution inputs to deal with photometric variations at different scales,
and the pixel-based deformation field learns RGB differences which result from
the deviations between the real-world and simulated degradations at arbitrary
coordinates. Extensive experiments show our trained model achieves
state-of-the-art performance on the RealArbiSR and RealSR benchmarks for
real-world scale arbitrary super-resolution. Our dataset as well as source code
will be publicly available.