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.

翻译：由于人脸具有高度结构化的特征，其在盲图像超分辨率任务中比自然场景更容易恢复。因此，我们可以从低质量图像与恢复后的人脸配对中提取图像的退化表征。利用该退化表征，可合成逼真的低质量图像，进而微调面向真实低质量图像的超分辨率模型。然而，这一过程既耗时又费力，且恢复人脸与真实人脸之间的差异进一步增加了优化的不确定性。为实现针对特定图像退化的高效模型适配，我们提出名为MetaF2N的方法，该方法在元学习框架下利用图像中包含的人脸来微调模型参数，使其适应整张自然图像。MetaF2N因此规避了退化提取与低质量图像合成步骤，仅需一次微调即可获得理想性能。考虑到恢复人脸与真实人脸之间的差距，我们进一步部署MaskNet自适应预测各位置的损失权重，以降低低置信度区域的影响。为评估所提MetaF2N方法，我们收集了包含单张或多张人脸的实景低质量数据集，实验表明MetaF2N在合成数据集与实景数据集上均取得了优越性能。源代码、预训练模型及收集的数据集已开源至https://github.com/yinzhicun/MetaF2N。