MetaF2N: Blind Image Super-Resolution by Learning Efficient Model
  Adaptation from Faces
Zhicun Yin Ming Liu Xiaoming Li Hui Yang Longan Xiao Wangmeng Zuo
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.