  Although single-image super-resolution (SISR) methods have achieved great
success on single degradation, they still suffer performance drop with multiple
degrading effects in real scenarios. Recently, some blind and non-blind models
for multiple degradations have been explored. However, those methods usually
degrade significantly for distribution shifts between the training and test
data. Towards this end, we propose a conditional meta-network framework (named
CMDSR) for the first time, which helps SR framework learn how to adapt to
changes in input distribution. We extract degradation prior at task-level with
the proposed ConditionNet, which will be used to adapt the parameters of the
basic SR network (BaseNet). Specifically, the ConditionNet of our framework
first learns the degradation prior from a support set, which is composed of a
series of degraded image patches from the same task. Then the adaptive BaseNet
rapidly shifts its parameters according to the conditional features. Moreover,
in order to better extract degradation prior, we propose a task contrastive
loss to decrease the inner-task distance and increase the cross-task distance
between task-level features. Without predefining degradation maps, our blind
framework can conduct one single parameter update to yield considerable SR
results. Extensive experiments demonstrate the effectiveness of CMDSR over
various blind, even non-blind methods. The flexible BaseNet structure also
reveals that CMDSR can be a general framework for large series of SISR models.
Our code is available at \url{https://github.com/guanghaoyin/CMDSR}.
