  Due to their highly structured characteristics, faces are easier to recover
than natural scenes for blind image super-resolution. Therefore, we can extract
the degradation representation of an image from the low-quality and recovered
face pairs. Using the degradation representation, realistic low-quality images
can then be synthesized to fine-tune the super-resolution model for the
real-world low-quality image. However, such a procedure is time-consuming and
laborious, and the gaps between recovered faces and the ground-truths further
increase the optimization uncertainty. To facilitate efficient model adaptation
towards image-specific degradations, we propose a method dubbed MetaF2N, which
leverages the contained Faces to fine-tune model parameters for adapting to the
whole Natural image in a Meta-learning framework. The degradation extraction
and low-quality image synthesis steps are thus circumvented in our MetaF2N, and
it requires only one fine-tuning step to get decent performance. Considering
the gaps between the recovered faces and ground-truths, we further deploy a
MaskNet for adaptively predicting loss weights at different positions to reduce
the impact of low-confidence areas. To evaluate our proposed MetaF2N, we have
collected a real-world low-quality dataset with one or multiple faces in each
image, and our MetaF2N achieves superior performance on both synthetic and
real-world datasets. Source code, pre-trained models, and collected datasets
are available at https://github.com/yinzhicun/MetaF2N.
