  Collecting amounts of distorted/clean image pairs in the real world is
non-trivial, which seriously limits the practical applications of these
supervised learning-based methods on real-world image super-resolution
(RealSR). Previous works usually address this problem by leveraging
unsupervised learning-based technologies to alleviate the dependency on paired
training samples. However, these methods typically suffer from unsatisfactory
texture synthesis due to the lack of supervision of clean images. To overcome
this problem, we are the first to have a close look at the under-explored
direction for RealSR, i.e., few-shot real-world image super-resolution, which
aims to tackle the challenging RealSR problem with few-shot distorted/clean
image pairs. Under this brand-new scenario, we propose Distortion Relation
guided Transfer Learning (DRTL) for the few-shot RealSR by transferring the
rich restoration knowledge from auxiliary distortions (i.e., synthetic
distortions) to the target RealSR under the guidance of distortion relation.
Concretely, DRTL builds a knowledge graph to capture the distortion relation
between auxiliary distortions and target distortion (i.e., real distortions in
RealSR). Based on the distortion relation, DRTL adopts a gradient reweighting
strategy to guide the knowledge transfer process between auxiliary distortions
and target distortions. In this way, DRTL could quickly learn the most relevant
knowledge from the synthetic distortions for the target distortion. We
instantiate DRTL with two commonly-used transfer learning paradigms, including
pre-training and meta-learning pipelines, to realize a distortion
relation-aware Few-shot RealSR. Extensive experiments on multiple benchmarks
and thorough ablation studies demonstrate the effectiveness of our DRTL.
