Learning to Super-Resolve Blurry Face and Text Images

Learning to Super-Resolve Blurry Face and Text Images

Xu X, Sun D, Pan J, Zhang Y, Pfister H, and Yang M.

(Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017.)

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 train- ing 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.