  Interactive image restoration aims to restore images by adjusting several
controlling coefficients, which determine the restoration strength. Existing
methods are restricted in learning the controllable functions under the
supervision of known degradation types and levels. They usually suffer from a
severe performance drop when the real degradation is different from their
assumptions. Such a limitation is due to the complexity of real-world
degradations, which can not provide explicit supervision to the interactive
modulation during training. However, how to realize the interactive modulation
in real-world super-resolution has not yet been studied. In this work, we
present a Metric Learning based Interactive Modulation for Real-World
Super-Resolution (MM-RealSR). Specifically, we propose an unsupervised
degradation estimation strategy to estimate the degradation level in real-world
scenarios. Instead of using known degradation levels as explicit supervision to
the interactive mechanism, we propose a metric learning strategy to map the
unquantifiable degradation levels in real-world scenarios to a metric space,
which is trained in an unsupervised manner. Moreover, we introduce an anchor
point strategy in the metric learning process to normalize the distribution of
metric space. Extensive experiments demonstrate that the proposed MM-RealSR
achieves excellent modulation and restoration performance in real-world
super-resolution. Codes are available at
https://github.com/TencentARC/MM-RealSR.
