Learning to Super-Resolve Blurry Face and Text Images

Xiangyu Xu, Deqing Sun, Jinshan Pan, Yujin Zhang, Hanspeter Pfister, Ming-Hsuan Yang; The IEEE International Conference on Computer Vision (ICCV), 2017, pp. 251-260


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.

Related Material

author = {Xu, Xiangyu and Sun, Deqing and Pan, Jinshan and Zhang, Yujin and Pfister, Hanspeter and Yang, Ming-Hsuan},
title = {Learning to Super-Resolve Blurry Face and Text Images},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {Oct},
year = {2017}