Learning to Super-Resolve Blurry Face and Text Images

We present an algorithm to directly restore a clear high-resolution image from a blurry low-resolution input. This problem is highly ill-posed and the basic assumptions for existing super-resolution methods (requiring clear input) and deblurring methods (requiring high-resolution input) no longer hold. We focus on face and text images and adopt a generative adversarial network (GAN) to learn a category-specific prior to solve this problem. However, the basic GAN formulation does not generate realistic high-resolution images. In this work, we introduce novel training losses that help recover fine details. We also present a multi-class GAN that can process multi-class image restoration tasks, i.e., face and text images, using a single generator network. Extensive experiments demonstrate that our method performs favorably against the state-of-the-art methods on both synthetic and real-world images at a lower computational cost.


Xiangyu Xu (Tsinghua University)
Deqing Sun (NVIDIA)
Jinshan Pan (Nanjing University of Science & Technology)
Yujin Zhang (Tsinghua University)
Hanspeter Pfister (Harvard University)
Ming-Hsuan Yang (University of California, Merced)

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