从人脸到自然图像:学习盲图像超分辨率的真实退化

Xiaoming Li, Chaofeng Chen, Xianhui Lin, W. Zuo, Lei Zhang
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引用次数: 8

摘要

如何设计合适的训练对对于超分辨真实世界低质量(LQ)图像是至关重要的,它既难以获得成对的高质量(HQ)图像,也难以合成逼真的退化LQ观测值。最近的研究主要集中在用手工制作的或估计的退化参数来建模退化,然而,这些方法无法模拟复杂的现实世界的退化类型,导致质量提高有限。值得注意的是,LQ人脸图像可能具有与自然图像相同的退化过程,通过利用其强结构先验,可以鲁棒地恢复具有逼真纹理的图像。这促使我们使用真实世界的LQ人脸图像及其还原的HQ对应图像来模拟复杂的真实世界退化(即ReDegNet),然后将其转移到HQ自然图像中以合成其真实的LQ对应图像。通过将这些配对的HQ-LQ人脸图像作为输入,明确预测退化感知和内容无关的表征,我们可以控制退化图像的生成,随后将这些退化表征从人脸转移到自然图像中,以合成退化的LQ自然图像。实验表明,我们的ReDegNet可以很好地学习人脸图像的真实退化过程。用我们的合成对训练的恢复网络对sota表现良好。更重要的是,我们的方法提供了一种新的方法来处理现实世界的复杂场景,通过学习面部部分的退化表示,可以显著提高非面部区域的质量。源代码可从https://github.com/csxmli2016/ReDegNet获得。
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From Face to Natural Image: Learning Real Degradation for Blind Image Super-Resolution
How to design proper training pairs is critical for super-resolving real-world low-quality (LQ) images, which suffers from the difficulties in either acquiring paired ground-truth high-quality (HQ) images or synthesizing photo-realistic degraded LQ observations. Recent works mainly focus on modeling the degradation with handcrafted or estimated degradation parameters, which are however incapable to model complicated real-world degradation types, resulting in limited quality improvement. Notably, LQ face images, which may have the same degradation process as natural images, can be robustly restored with photo-realistic textures by exploiting their strong structural priors. This motivates us to use the real-world LQ face images and their restored HQ counterparts to model the complex real-world degradation (namely ReDegNet), and then transfer it to HQ natural images to synthesize their realistic LQ counterparts. By taking these paired HQ-LQ face images as inputs to explicitly predict the degradation-aware and content-independent representations, we could control the degraded image generation, and subsequently transfer these degradation representations from face to natural images to synthesize the degraded LQ natural images. Experiments show that our ReDegNet can well learn the real degradation process from face images. The restoration network trained with our synthetic pairs performs favorably against SOTAs. More importantly, our method provides a new way to handle the real-world complex scenarios by learning their degradation representations from the facial portions, which can be used to significantly improve the quality of non-facial areas. The source code is available at https://github.com/csxmli2016/ReDegNet.
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